B825 unit 3 07e by Haider Ali

Marketing in a Complex World

Unit 3

Understanding the Marketing Environment Prepared for the Course Team by Haider Ali, P.K. Viswanathan and Tony Stapleton

Masters

CORE COURSE TEAM Haider Ali, Course Team Chair and Author

Claudia Simoes, Author

Jo Woods, Course Manager

Tony Stapleton, Author

Sally Dibb, Author, Course Team Member Keith Honnor, Course Manager (IT)

External assessor

Karen Kingsnorth, Programme Manager

Professor Malcolm McDonald, Cranfield University

Judith Margolis, Author, Course Team Member

Production Team

Vyv Pettler, Course Team Member

Martin Brazier, Graphic Designer

Terry Oâ&#x20AC;&#x2122;Sullivan, Author

Helen Coolen, Editor

Gareth Stone, Course Team Member

Jenny Edwards, OUBS Quality Improvement

Andrew Lindridge, Course Team Member

Julie Fletcher, LTS Project Manager

Jeanne Barby, Course Team Assistant

External authors

Jenny Gray, Software Developer Dave Richings, Print Buying Co-ordinator

Ian Reid, Author

Diane Hopwood, Compositor

Palamadai Krishnan Viswanathan, Author

Roy Lawrance, Graphic Artist

Frederick Thomson, Author

David Massey, LTS Quality Assurance

Jill Winter, Author

Katie Meade, Rights Executive

Leslie de Chernatony, Author

Grant Miller, Project Officer

Fiona Harris, Author

Holly Clements, Media Assistant

David Mercer, Author

This publication forms part of an Open University course B825. [The complete list of texts which make up this course can be found at the back (where applicable)]. Details of this and other Open University courses can be obtained from the Student Registration and Enquiry Service, The Open University, PO Box 197, Milton Keynes MK7 6BJ, United Kingdom: tel. +44 (0)870 333 4340, email general-enquiries@open.ac.uk Alternatively, you may visit the Open University website at http://www.open.ac.uk where you can learn more about the wide range of courses and packs offered at all levels by The Open University. To purchase a selection of Open University course materials visit http://www.ouw.co.uk, or contact Open University Worldwide, Michael Young Building, Walton Hall, Milton Keynes MK7 6AA, United Kingdom for a brochure. tel. +44 (0)1908 858785; fax +44 (0)1908 858787; email ouwenq@open.ac.uk The Open University Walton Hall, Milton Keynes MK7 6AA First published 2004. Reprinted 2007. Copyright # 2007 The Open University All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, transmitted or utilised in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without written permission from the publisher or a licence from the Copyright Licensing Agency Ltd. Details of such licences (for reprographic reproduction) may be obtained from the Copyright Licensing Agency Ltd, Saffron House, 6â&#x20AC;&#x201C;10 Kirby Street, London EC1N 8TS; website http://www.cla.co.uk/. Open University course materials may also be made available in electronic formats for use by students of the University. All rights, including copyright and related rights and database rights, in electronic course materials and their contents are owned by or licensed to The Open University, or otherwise used by The Open University as permitted by applicable law. In using electronic course materials and their contents you agree that your use will be solely for the purposes of following an Open University course of study or otherwise as licensed by The Open University or its assigns. Except as permitted above you undertake not to copy, store in any medium (including electronic storage or use in a website), distribute, transmit or retransmit, broadcast, modify or show in public such electronic materials in whole or in part without the prior written consent of The Open University or in accordance with the Copyright, Designs and Patents Act 1988. Edited and designed by The Open University. Typeset in India by Alden Prepress Services, Chennai. Printed and bound in the United Kingdom by the Charlesworth Group, Wakefield. ISBN 978 0 7492 2648 0 1.1

CONTENTS 1

Introduction 1.1

Aims and objectives

An overview of marketing research

2.1

What is marketing research?

2.2

Why should you use marketing research?

2.3

Role of marketing research

2.4

Marketing research process

2.5

Ethical issues in marketing research

Problem definition and research objectives

3.1

Rationale for a good problem definition

3.2

The key elements of problem definition

3.3

Marketing problem and research problem

3.4

Research objectives

Exploratory research

4.1

Exploratory research

Exploratory research â&#x20AC;&#x201C; qualitative research

5.1

Conducting qualitative research

5.2

Focus groups

5.3 In-depth interviews

5.4 Projective techniques

5.5 Implications of the internet on qualitative data

collection

Conclusive research â&#x20AC;&#x201C; descriptive and causal

6.1

Descriptive designs

6.2

Causal research (experimental research)

Technical aspects of undertaking research

7.1

Why do we need sampling?

7.2

Types of sampling

7.3

Non-probability sampling

7.4

Data measurement

7.5

Survey questions

7.6

Data collection

Data analysis

8.1

Descriptive statistics

8.2

Inferential statistics

8.3

An overview of data analysis tools

Conclusions: market research

10 Historical background of scenario planning 11 Forecasting

101

11.1 The forecasting process

102

11.2 Short- and medium-term forecasting

103

11.3 Long-range forecasting

105

12 Historical development of scenarios

111

12.1 The emergence of scenario analysis

111

12.2 The use of scenarios

114

12.3 A simple approach to scenarios

117

13 Analysing the environment

119

13.1 Step 1: Environmental analysis

120

13.2 Step 2: Identify clusters

120

14 Preparing scenarios

123

14.1 Step 3: Select drivers and develop frameworks

123

14.2 Step 4: Identify scenarios

128

14.3 Step 5: Write the scenarios

130

14.4 Step 6: Identify key issues

133

15 Formulating strategy

135

16 Conclusion: Scenario planning

139

Appendices (for scenario planning)

140

References

166

Acknowledgements

168

1 INTRODUCTION

INTRODUCTION

In this unit we will look at marketing research then we will look at scenario planning. The common element of both activities is the usage of information as a means of dealing with the uncertainties presented by the marketing environment. However, the methodologies used by the two disciplines, as well as the nature of the insights they yield about the marketing environment, are radically different. We start with marketing research because it deals with the basics of collecting and analysing data. Scenario planning, on the other hand, deals with the long-term aspects of marketing, best described as long-range marketing. This is a relatively new term to describe the planning process that takes account of activities in the ‘far environment’. It therefore encompasses developments across society as a whole and often in the longer-term future of 10 years or more. This approach is in contrast to traditional marketing, which tends to be limited to commercial activities over the next three to five years. Long-range marketing should be a key element of corporate planning, and the two approaches combine to produce robust strategies. These are designed to safeguard the long-term survival of the organisation, and they are as relevant to public and voluntary organisations as they are to those in the commercial sector. Many organisations, and sometimes whole industries, have been destroyed by long-term changes they failed to recognise. One has only to think of the recent changes in banking or retailing to see how even the largest companies have found their positions undermined by these hidden forces. The term ‘long-range marketing’ may be new but the concepts have long been understood by those marketing managers who have – correctly – seen their role as including involvement in corporate strategy processes. The specific problem, which this unit addresses, has been that – immersed in shorter-term problems – managers are forced to focus on shorter-term solutions. This unit intends to redress the balance and to demonstrate that marketing is even more important over the longer term than it is in the short term (Pfeffer and Salancik, 1978). Unit 1 summed up the importance of marketing research under the subheading ‘Research the market to understand your customers’ needs’. The focus on customers’ needs becomes even more complex given today’s highly competitive and global environment where customers’ expectations are increasing at a rapid pace and complementing that growth is an increasing array of

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tools and methodologies with which marketers can collect and analyse information. Moreover, we have seen in the previous unit the importance of considering the non-linearity of relationships in complex systems, and the difficulties of imposing too much structure when competing in a volatile environment. This poses an important challenge for managers undertaking marketing research. As we shall see, the quality of research can be associated with methods that emphasise the importance of being systematic and following what, at times, can seem to be a very linear approach. However, as we discuss the different ways in which research can be done, it should become clear that while good research should be rigorous, the extent to which it should follow a linear path can be dependent on the nature of the research problem being addressed. Marketing research seeks to provide marketers with a set of techniques for collecting, analysing and interpreting data into actionable recommendations. In this unit we will look not only at how the above can be done, but we will also deal with the insights these tools can offer and also their limitations. We appreciate that many people reading about marketing research may not actually need to use the methodologies themselves. Rather most managers may, at most, be involved in either commissioning research or more likely reading research results â&#x20AC;&#x201C; and making use of them in decision making. Given this situation, our emphasis tends towards giving an overview of techniques and methods rather than technical details. However, those details are given where an understanding will help to appreciate the differences in the insights afforded by different approaches and their limitations. Moreover, as Geraldine Lillis says, marketers today can be actively involved in gathering data. Some client organisations have redesignated their market researchers as â&#x20AC;&#x2DC;Consumer Insightâ&#x20AC;&#x2122; departments, implying a change of focus, not simply a change of name, and indicating an active desire to achieve greater understanding of the consumers to whom the company wishes to market its products and services. Brand managers in some companies now routinely spend time interacting directly with their target consumers, and client-side researchers are engaged in group moderation, rather than obtaining their view entirely through the eyes of a qualitative researcher and/or the mirror of a viewing facility. Lillis 21:2002

Figure 1.1 illustrates the structure of the market research part of this unit. In Section 2 we start by examining what marketing research is and how it can be used by marketers. The section then maps out the different stages of the marketing research process and concludes with an examination of the ethics of marketing research.

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1 INTRODUCTION

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Figure 1.1

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An overview of the market research process

Section 3 shows the importance of defining the research problem and associated research objectives. Whether or not an organisation decides to use an external research agency, its managers may well need to be able to understand how the ‘decision problems’ they face can be translated into research problems – since it is answers to these that they will have to use. In Section 4 we deal with exploratory research (where researchers try and find out simply ‘what exists’, rather than ‘how much’). For example, exploratory research can try and deal with the range of customer attitudes towards a product, rather than measuring what percentage of people hold a particular attitude. Such research can be undertaken using pre-existing research reports, also referred to as secondary data. We specifically look at the different types of secondary data and how they can be used in a research project. Secondary research can be acquired from internal sources within the organisation or sources external to the organisation. You should note that secondary research can be qualitative i.e. citing ideas and opinions that do not aim to be representative of a wider

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population, but it can also include the reporting of conclusive research – for example statistics reporting consumer surveys.

EXAMINING EXISTING RESEARCH DATA TO HELP WITH PRIMARY RESEARCH An organisation that helps people to stop smoking ran presentations in schools telling pupils about the dangers of smoking. At each presentation there would typically be about 200 pupils. In order to evaluate these sessions questionnaires were handed out after the presentations in order to assess how much pupils had understood and been influenced by the presentations. Given the limited amount of time available the questionnaires were very short. As an example of secondary research, some of the questionnaire items that were used were based on items that had been used by other organisations in their surveys of young peoples’ smoking behaviour. Those questions were then adapted for use in this evaluation.

Section 5 continues with the theme of exploratory research but moves the focus to primary research. Such research is undertaken specifically for the research project in question. This gives the marketer a greater ability to focus on issues that are of interest to them – by definition one of the factors constraining the usefulness of secondary research is that the marketer can only use what has already been published. Qualitative research can be undertaken using focus groups, in-depth interviews and projective techniques, for example. In this section we also deal with the usage of observation as a means of data collection. Section 6 deals with conclusive research, and covers both descriptive and causal methods. The choice of research design can be either exploratory or conclusive (the latter dealing with quantification) – though designs can often have an initial exploratory stage followed by a conclusive stage. In some instances the findings from conclusive research may be unclear, so an exploratory stage may also be required at the end of research in order to ‘explore’, for example, via focus groups, the meanings behind the quantitative results. The above discussion identifies the main research design options available to an organisation – as illustrated by the exploratory and conclusive research ‘branches’ in Figure 1.1. Within these broad designs there will need to be decisions about who should be questioned, the sample for the study. There will also be issues surrounding data measurement, the design of questionnaires, analysing the data and the collection of data. These technical aspects of undertaking marketing research are illustrated at the bottom of Figure 1.1. They apply to both exploratory and conclusive research ‘branches’ in Figure 1.1. They are discussed in Section 7. 8

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In Section 8 we deal with data analysis in conclusive research and briefly explain some of the analytical tools that are used in marketing research. Section 9 completes the part of this unit devoted to market research. Sections 10 to 16 of this unit deal with scenario planning (written by Tony Stapleton). Our coverage of scenario planning starts by examining the uncertainty which is inevitably posed for long-range planning by the rapidly changing environment in which organisations now operate. Recognition of these external changes is one of the most important reasons for doing the environmental analysis you looked at in Unit 1. We then consider how organisations can plan under these volatile conditions. Starting with a brief review of approaches to forecasting, we specifically focus on scenario analysis as the easiest and most generally applicable technique for handling such uncertainty. You are encouraged to apply scenario analysis to your own work situation. To conclude this introduction to Unit 3 we will define some key terms which will be used at various points in this unit, and whose meanings are important.

Case study: a comprehensive description and analysis of a single situation. Causal research uses specific hypotheses which are designed to test whether one construct under certain conditions causes a change in another construct. Coding refers to the categorisation and numbering of responses. Construct: a concept, usually psychological, such as attitudes and values, that is not directly observable. Construct validity refers to the ability of a construct to measure a concept. Control group: a group of subjects who are not exposed to the experimental treatment. Data refers to unassimilated facts about the market. Delphi approach: a method whereby each member of a group is given the chance to change their opinion in the light of the opinions of other people in the group. Descriptive research summarises some aspect of the environment â&#x20AC;&#x201C; where the hypotheses are tentative and speculative. Face validity: the validity of a measurement instrument as determined by subjective argument or judgement. Focus group: a group discussion focused on a series of topics introduced by a discussion leader. Participants are encouraged to voice their own opinions and to react to those of others. Information refers to data that have been transformed into answers for the specific questions of decision makers.

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Categorical variables can be nominal (where the order does not matter) e.g. male/female or ordinal (where the order does matter) e.g. disagree, neutral, agree. Quantitative variables can be continuous or discrete. Continuous variables can take any values in a given range e.g. height. Discrete variables can only be whole numbers e.g. the number of children in a family.

1.1

AIMS AND OBJECTIVES

The aims of this unit are: l

to demonstrate that marketing research is an extremely powerful tool that can aid marketing decision makers

to provide readers with a systematic approach to doing marketing research with an applied orientation so that at the micro (organisational) level they would be in a position to take decisions with minimum risk

to explain the background to the uncertainties arising from the complex external environment in which organisations operate

to introduce you to the main forms of long-range forecasting

to explain and discuss scenario analysis

to demonstrate how scenario analysis can be used to develop robust strategies.

After studying this unit, you will be able to: l

define research problems/opportunities in the context of marketing decision situations

identify and enumerate information requirements in accordance with clearly defined research objectives

understand the role of different research designs and the different insights that they can offer into marketers’ decision making

understand the practicalities of a range of different data collection methods together with an understanding of the insights that they can offer and their shortcomings

appreciate the use and limitations of some data analysis tools

understand why long-range forecasting is needed to deal with external uncertainty

appreciate the main approaches to long-range forecasting, especially scenario analysis

use simple scenario analysis as part of your own planning processes.

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AN OVERVIEW OF MARKETING RESEARCH

All marketing decisions involve uncertainty, as there is always the possibility that events will not turn out as expected. If this happens losses can be incurred and these can be more than just financial, for example, there could be a loss of reputation, alternative business opportunities foregone and so on. It is in order to manage uncertainty that organisations acquire information. Marketing research involves systematic ways of gathering and analysing market information. As we shall see later, the notion of acquiring information in order to minimise losses arising from detrimental, uncertain events is an important issue. It means that questions relating to the extent to which research is undertaken can involve assessing the costs of the research in comparison to the benefits that may result from it.

MANAGEMENT OF MARKETING RESEARCH SHOULD BE SYSTEMATIC TOO... ‘We tend to be great at measuring things, at collecting market data... but we are not very disciplined when it comes to using this data to drive decision making...we had evidence coming in from market research that contradicted some of the fundamental assumptions we had about our brand and our customers. The problem was that this evidence was all over the place – no one was really looking at the big picture. As a result it was some time before we started to take notice.’ ‘Starbucks – delivering customer service Moon’, Y. and Quelch, J. 2004, 9-504-016 pp. 9-10

We will develop the ideas as to what can actually count as marketing research once we have attempted to define it.

2.1

WHAT IS MARKETING RESEARCH?

Marketing research is the function that links an organisation to its market through information. This information is used to identify and define marketing opportunities and problems; generate, refine, and evaluate marketing actions; monitor marketing performance; and improve the understanding of marketing as a process. Marketing research specifies the information required to

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address these issues; designs the method for collecting information; manages and implements the data-collection process; interprets the results; and communicate the findings and their implications. American Marketing Association (Official Definition of Marketing Research)

From this definition we can identify the following important characteristics of marketing research: Marketing research involves systematic collection, classification, analysis, and interpretation of data that will aid decision makers who are concerned with marketing goods, services, or ideas. Within this description clearly there are a number of different types of study that we can validly categorise as being marketing research. Alan Andreasenâ&#x20AC;&#x2122;s view is that any reliable information that improves marketing decisions can be considered marketing research. For example, instead of an expensive survey (involving large numbers of questionnaires being mailed out to respondents) to investigate a research problem, a marketer could alternatively test their product in representative markets. This has the advantage of telling the firm what people will do as opposed to what they say they will do, and it also will be significantly cheaper than the mail-based survey. When undertaking research into the choice of images for advertising, the display of a variety of images to a small but representative set of target candidates can help to identify any obvious problems. This is an important issue and we shall explore it a little further here, since it emphasises that good quality marketing research requires adherence to certain principles of scientific enquiry rather than usage of well known data collection techniques, such as survey questionnaires. We think that this is important because it offers managers the chance to consider a wider range of data collection possibilities which may be more practical, insightful and cheaper than the traditional alternatives. In addition to the techniques presented above there are other alternatives to surveys as well. In retailing, monitoring changes in pedestrian and vehicle traffic counts near retail outlets can help to assess the success of a competitorâ&#x20AC;&#x2122;s new offering. The relative importance of product features can be assessed by recording the questions customers ask about the product. These activities would still count as marketing research rather than casual observation as long as there is careful specification of the necessary observations, systematic observation, and observation at random times and places and where possible by a variety of observers. Careful monitoring, recording and analysis of the results are also important. As we shall see later, an important component of a research project can be secondary research. However, it is possible in some situations for secondary sources of information to be totally adequate for a managerâ&#x20AC;&#x2122;s research needs and for the manager not to require any additional primary research. For example, an art museum wanted to show sponsors that it was planning a 12

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programme to expose kids from low income households to the art world. The plan was to invite sponsors to the first night of the children’s encounter with culture. However, an article in a trade publication that had reviewed the value of such programmes amongst other museums showed that such events had not been successful at all. Given the straightforward nature of the decision, secondary information was enough to make the final decision. Organisations can also benefit from systematic observation, for example, by varying the themes in routine fundraising mailings. Convenience samples (described in Section 7) can be used, where they are undertaken in the form of the systematic querying of customer contacts. Such sampling is very appropriate for exploratory research designs. However it is important that managers are aware of underlying biases. In the previous paragraphs you will have seen some reference to the importance of research being ‘reliable’. This term has a specific and important meaning in marketing research. The methods used to gather and analyse data are said to be reliable if they return the same results time after time – assuming of course that no real change has taken place. You can use an analogy with a thermometer. You would expect it to show the same temperature no matter how many readings were taken (assuming of course that the actual temperature had remained constant). Moreover, it is important that the methods which are used have validity, i.e. that they accurately measure what they are supposed to measure. So, for example, if a questionnaire is used to assess individuals’ attitudes towards a brand – the questions which are used should actually be doing this. This is the reason why marketers need to be very careful about the questions that are asked of respondents and why questionnaires are often piloted, analysed, amended and then finally used in an actual survey.

EXAMPLE 2.1 HOW INFORMATION SHOULD BE USED EFFECTIVELY Terry’s, a British chocolate manufacturer, used extensive market research to identify the ‘right’ concept for a new type of chocolate product. Market research sources included use and attitude surveys, qualitative and quantitative research reports, Euromonitor reports, consumer discussions and store surveys. The main thrust of research for product development was focus group discussions in which consumers provided insight into the motivations behind buying chocolate products, their feelings towards chocolate in general, and toward mint and other flavours in particular; attitudes to examples of different products; frequency of purchase; reasons for purchase and reasons for consumption. Focus groups helped the company to determine specific product features, such as the colour or name of a new brand.

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In addition to extensive consumer research Terry’s had undertaken extensive competitive analysis, both by asking consumers to compare Terry’s products with the competition and by monitoring their competitors’ market positions and product impact. Such research was carried out on an ongoing basis. Source: ‘Terry’s Group: Designing novelty chocolates’, Bruce M., Design Management Institute 2001.

ACTIVITY 2.1 Identify the relevant information that a marketing manager might be looking for while trying to make effective decisions regarding new product development and pricing.

COMMENTARY The aim of any professional marketing manager is to minimise the risk of making a wrong decision that could adversely impact the bottom line of the organisation. New product development envisages information regarding customer needs, their perceptions about the product, and their buying behaviour. It is crucial to know what strategies the competitors would use and the manner in which they are likely to affect our product performance in the marketplace. As far as pricing decisions are concerned, information about current and expected product cost in terms of fixed and variables cost, the likely price the competitor may charge, the likely volume of sales we might get, the minimum price we might accept that would at least cover the variable cost of the product in the introduction stage of the product, and an assessment of the cost structure of the key competitors. Despite the importance of marketing research, and the prominence it receives in all marketing texts, there are some common misconceptions. Alan Andreasen (1983) says that, for example, it is commonly believed that marketing research is only needed where you have significant decisions to undertake and that research only refers to large scale field work studies involving large samples. Andreasen proposes that the value of research should be viewed on the basis of the cost vs. the benefits. The costs can be to do with, for example, not just the financial costs of doing the research, but also the costs related to delaying decisions while the survey results are collected. On the other hand, there can be situations where there is not a great deal at stake, but the research can be undertaken very inexpensively and quickly.

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2.2

WHY SHOULD YOU USE MARKETING RESEARCH?

Marketing research can address a wide range of questions ranging from finding out the market share of a company in a particular geographic region, to identifying the individuals who make the purchasing decisions for the types of products sold by a particular company. Marketing research can also be used to evaluate customer satisfaction and the performance of competitors. So marketing research is not just concerned with finding out information about the sales potential for products and services, it can be used to inform marketers about all aspects of their marketing environment.

IDENTIFYING THE RIGHT CUSTOMER FOR FORD’S KA IN FRANCE Before Ford could develop a marketing strategy for Ka (a new small car) in France, they needed to determine the target market for the new product. The car industry had traditionally segmented the market into size-tiers but Europe’s market for small cars was changing rapidly, prompting significant concerns about continuing with ‘traditional’ size-based market segmentation. Ultimately the firm decided to target market segments they labeled as ‘Freedom Lovers’ and ‘Attention Seekers’. These were customers who were aged between 25 and 35 years old, urban and with a strong inclination towards products and innovation. Once this segment had been identified additional research showed that these people were heavy consumers of movies and magazines, but watched television much less often than the average consumer. This information had an impact on the choice of advertising strategy with a focus on cinema advertising. ‘Ford Ka, The market research problem’, Cothier, G., Christen, M. and Soberman D. 2003 Case no. 503-084-1

Typical reasons for using marketing research In addition to marketing research being able to answer a range of questions, there are also a range of reasons why it should be used: l

The cost of making a wrong decision is generally much greater than the cost of doing marketing research to confirm or disprove marketing managers’ conjectures.

In a highly competitive domestic and global marketplace, it is crucial to track changing trends in the marketplace and competitors’ activities by providing systematic feedback information. This would help organisations counteract existing and potential competition and position their products and services strategically.

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Managers need to substantiate their proposals regarding products, ideas or marketing programmes with relevant data before taking them to top management for approval.

Reasons for product failures in the recent past are unknown and the reasons need to be investigated before future products can be launched.

What specific steps should a firm take to retain customers?

What are the differences between domestic customers and international customers so that appropriate marketing plans can be developed for the global marketplace?

ACTIVITY 2.2 Before Microsoft entered the South Korean market with its Xbox games console it undertook marketing research. The background to the situation was as follows. What research do you think the company should undertake before entering the market?

The video game console industry had been composed of console manufacturers, software developers/publishers and ancillary equipment providers. Console manufacturers such as Sony and Microsoft produced hardware platforms for video games. Games developers such as Electronic Arts and Activision created and marketed video games. Console makers recovered losses from console production by creating profitable software for their systems and extracting royalties from third party publishers who made games for their systems. Seen as important to the success of a console was the availability of highly regarded games that could only be played on that console. While PC gaming is phenomenally strong in Korea, console gaming is almost non-existent. This means that Microsoft would have to spend a lot in educating the market, but on the other hand Koreans love playing games online. The total size of the Korean market was estimated at $2.8billion in 1982 compared to $10billion in the US. Games are not associated with the stigma that affects them in other markets. Growth opportunities were therefore huge. Microsoft felt that it could also learn from Sony’s experience with the launch of the Playstation console. Sony treated Korea as an extension of the Japanese market and went for the mass market rather than hard core gamers. ‘The Xbox launch in Korea’, Descamps, F. Pennya C. April 2003, Case M-303

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COMMENTARY Amongst the initial issues that Xbox tried to address was the selection of games for the Korean launch of the Xbox. The company also tried to determine the importance of localisation for the Korean market. Localisation refers to customising games for the home market by converting documents and in-game dialogue to the host countryâ&#x20AC;&#x2122;s language. Microsoft also needed to profile and analyse the target segments in the market, which would help to prioritise the segments to target and how to reach them.

2.3

ROLE OF MARKETING RESEARCH

The following table shows the role of marketing research in decision making. Role of marketing research in decision making l

Identifying market opportunities and associated constraints

Developing and implementing marketing programmes

Evaluating the effectiveness of marketing programmes

Identifying marketing opportunities and associated constraints This is a first step towards developing a marketing strategy. Letâ&#x20AC;&#x2122;s take the example of a firm interested in launching new products in existing markets or entering new markets with existing products. The key issues would be: Who are our main competitors and how strong are they in the marketplace? Are the customers fully satisfied with the current offerings or are there any unfulfilled needs? What are the perceptions of customers with regard to our products and services in relation to the competition?

Developing and implementing marketing programmes Marketing research will tell the firm whether the marketing mix is good enough to maximise benefits in terms of sales, profits, and market shares from the alternative opportunities available. Successful new product introduction heavily depends on the elements of its marketing mix, which in turn depends on the quality of the marketing research undertaken prior to the launch of the new product.

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Evaluating the effectiveness of marketing programmes Are the objectives of the marketing programmes being achieved? Do we require any corrective action? These two questions can be answered by getting feedback information from the marketplace. The feedback information is in terms of providing answers to questions such as the following: What is the current market share of our new product since its inception six months ago? Is it increasing or decreasing? What are the perceptions of the customers with regard to the performance of our product? Is the volume of purchase consistent with our target goals? If not, what are the reasons? Marketers have a range of methods with which such information can be gathered.

EVALUATING A TRAINING PROGRAMME Pharmacists at a leading British university wanted to develop a programme for advocacy workers (people who work with refugees, for example) in order to train them in coronary heart prevention. The aim was to make these people more literate in medical terms so that they could better translate interactions between non-English speaking patients and medical staff at UK hospitals. After having received the training the participants were given a questionnaire to complete â&#x20AC;&#x201C; this would help to evaluate the training they had received. A few months afterwards they were all telephoned and asked questions that focused more specifically on how they had used information provided in the training in their everyday work. Both types of data collection were qualitative. The focus was on peopleâ&#x20AC;&#x2122;s opinions and impressions rather than the percentages of respondents who held a particular opinion. This was mainly because the size of the total number of participants was very small. The second survey showed that despite the training sessions there was still some confusion about the role of cholesterol as a cause of heart disease and the training organisers decided to address this issue more clearly and explicitly in future sessions.

2.4

MARKETING RESEARCH PROCESS

The following diagram (Figure 2.1) gives a typical step-by-step approach to doing marketing research. While authors may differ in the number of steps used, the sequence is very important. Each step is briefly explained and is followed by an example that shows a typical way of conducting research.

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Problem definition and research objectives The problem referred to in the title above is usually a marketing problem faced by an organisation, such as a loss of sales. Alternatively it could be a marketing opportunity which the organisation would like to take advantage of. Problem definition is important because the initial motivation for the research may only be a symptom or part of a wider problem. For example, the loss of sales is likely to be a symptom of perhaps a problem in the marketing mix. When considering the problem marketers need to think about possible causes. Exploratory research may be required in order to identify these. Some problems may, of course, be too difficult or expensive to research.

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Another important factor in assessing a Figure 2.1 A typical step-by-step approach to research problem is that it should be related doing marketing research to a marketing decision – where managers face a range of alternative options. Marketing research in such an instance will help them to choose the most effective option. When the researcher is unsure how to precisely define the problem, secondary data analysis and qualitative research will help to clarify the key issues. In-depth interviews and focus group discussion in qualitative research are extremely useful in formulating the research problem and associated hypotheses.

Research design Research design focuses on what type of research is envisaged for the proposed marketing research project. Exploratory research generates a number of ideas/hypotheses and gives a sense of direction to the analyst in identifying the research problem. Descriptive research is part of conclusive research. As the very word descriptive suggests, such research involves describing the market characteristics that are of interest to us. For example, we may like to know the proportion of a particular class of people who would buy from department stores offering discounts. The other part of conclusive research is experimental or causal research. Depending upon the nature of the problem, the appropriate design will have to be selected.

Sampling design Sampling design deals with the method of selecting units of analysis such as households, people, consumers, and companies from a population (universe) of interest to a manager. It addresses OU BUSINESS SCHOOL

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the type of sampling (probability versus non-probability) to be adopted for collecting the data and also guidelines for determining the right sample size. The quality of the decisions one takes in marketing depends entirely on the quality of sampling adopted. The risk of making a wrong decision rises substantially if the sampling procedures employed are faulty.

Data collection Problem definition deals with “What do I want to know?” while data collection addresses the issue “How can I obtain the information required?” Primary data are collected by the organisation itself for a specific purpose of its own. The benefits of primary data are that they fit the manager’s needs exactly, and are up to date and reliable. Primary data can be generally collected through interviews or surveys. Secondary data refers to information collected by other organisations or for purposes other than the research problem immediately facing the organisation. Any data which are not collected by the organisation for the specified purpose, are secondary data. These may be published by other organisations, available from research studies, syndicated services, published by the government, and so on. Secondary data have the advantages of being much cheaper and faster to collect. An appropriate measurement scale for designing the questionnaire, and well executed fieldwork through, for example, the choice of an effective contact method (the telephone, postal mail and increasingly the web are common choices) are the cornerstones of a successful marketing research project.

Data analysis and interpretation Data analysis approaches vary depending on whether it is qualitative or quantitative data that is being analysed. For a quantitative survey we must first check the questionnaire (after the data collection) for completeness, consistency, and legibility. We might call this data purification or data filtering. The outcome of the analysis depends heavily on the quality of the input data. After filtering, data will be coded. Coding involves assigning a number to responses of every question. For example, consider a question where gender is to be used by the respondent. We may code 1 = Male and 2 = Female. The data are then analysed to throw light on the research study undertaken and thus form the input into marketing decisions. Interpretation of the results of data analysis is paramount to provide actionable recommendations.

Report presentation The final result of most projects will be the report and this has to be robust (in terms of the choice of appropriate methodologies) and written in a such a clear style that managers can understand and act on the key findings.

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EXAMPLE 2.2 MARKETING RESEARCH AT MARRIOTT CORPORATION Marriott Corporation, a leading hospitality company in the world, has three major types of business units: lodging, contract services, and restaurants. Its lodging operation is the most famous of all the three business units. Marketing Research at Marriott is conducted through its Corporate Marketing Services (CMS). CMS undertakes a variety of research projects. Methods employed include both qualitative and quantitative research approaches. Telephone surveys, mail surveys, personal surveys, focus groups, and customer intercepts are the methods employed to elicit information on market segmentation, product testing, price sensitivity analysis, and customer satisfaction. The research process is cohesive and logical at Marriott. The first step is to define the problem and the associated research objectives for the concerned business unit. The second step is to identify the right research design and plan the study. At this stage, CMS has to decide whether to conduct its own in-house research or employ an outside firm. If CMS decides to employ an outside firm, it should evaluate whether using multiple firms will be an advantage. Once the decision is reached, the research is executed in terms of data collection, analysis, and presentation of the findings of the study. The last step in the research process is to activate the dialogue mode between the business unit and CMS. This would help CMS in explaining the implications of the research findings to the business unit apart from making useful suggestions for the future course of action. Source: Adapted from ‘Listening to Customers: The Market Research Function at Marriott Corporation’, Marketing Research (March 1989) pp. 5–14

ACTIVITY 2.3 Analyse, criticise, and explain the following statement. “Marketing Research is a fishing expedition and merely an encyclopaedic gathering of assorted facts”.

COMMENTARY Marketing research is purposeful investigation that lays the structure for sound decisions. There are essentially three parts to any investigation. (1) The question asked. (2) The answer given. (3) The defensive mechanism.

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Consider the following simple example. The CEO of a company asks the Vice-President (Marketing): “What price should we charge for our new model car?” The Vice-President asks his Marketing Research Manager to provide an appropriate answer to this based on two or three alternatives. By analysing the relevant information, the marketing research manager recommends a price of $20,000 as the right choice. The answer given by the vice-president to the CEO is $20,000. The defensive mechanism in this context would be for the marketing research exercise to provide the answer to the question: “why this price”.

2.5

ETHICAL ISSUES IN MARKETING RESEARCH

Ethics addresses the issue whether moral principles and values that are associated with actions taken in a research project are adhered to by the three parties concerned namely: (1) the client who sponsors the research, (2) the provider of service who conducts the research project, and (3) the target audience or respondents who provide the information in the survey research.

Ethics to be adhered to by the sponsor There are a number of issues that affect sponsors of research. For example, the marketer should not abuse marketing research information. The client organisation is ethically bound not to abuse in any manner information obtained through market research projects. There is a saying “lies, damn lies and statistics”. This is an apt expression for misuse of research findings. For example, there could be a number of comparative advertisements or product performance claims that emanate from market data, which are not statistically significant. Given the lack of strength in the findings, they should not be used in marketing communications.

Ethics to be adhered to by the service provider The service provider in turn has to bear in mind a number of factors. For example, it is expected that they will execute the project in an objective manner free from biases. Inappropriate execution involves for example, biased sampling, deliberately ignoring relevant data, or complete misuse of statistics. All these will lead to erroneous conclusions.

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Respondents’ ethics and rights A respondent who, on his or her own volition agrees to participate in the research project, has the ethical responsibility to provide the service provider with honest and true answers. Giving a false answer to a sensitive question is unethical, whereas avoiding a sensitive question is not unethical. The respondents who participate in a research project have the following rights: l

the right to privacy

the right to safety

the right to know the real purpose of the research project

the right to the research outcome

the right to decide whether to answer a question.

ETHICAL CODES OF THE BRITISH PSYCHOLOGICAL SOCIETY The British Psychological Society (BPS) covers a number of different areas relevant to the conduct of surveys: (i)

Consent – the code lays down clear guidelines for obtaining consent for research and is particularly concerned to protect the interests of children or those who may have impairments that will limit their understanding.

(ii)

Deception – this is concerned with the withholding of information, the use to which the research will be put or the misleading of participants.

(iii)

Debriefing – the code requires researchers to discuss the research with participants to ensure that there are no negative effects or misconceptions.

(iv)

Withdrawal – researchers should make clear to the participants that they can withdraw at any time.

(v)

Confidentiality (defined as the agreements that limit others’ access to private information) – the code specifies that information obtained is confidential subject to the participants’ consent and to legal requirements such as the Data Protection Act.

(vi)

Protection of participants – researchers have a primary responsibility to protect participants from physical or mental harm during the investigations.

(vii) Observational research – the privacy and well-being of the participants must be protected and unless consent is given observational research is only acceptable where those observed would expect to be observed by strangers. (viii) Giving advice – during research the researcher may become aware of psychological or physical problems of which the participant is unaware; in such a case the researcher should inform the participant of such problems.

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(ix) Colleagues – researchers share responsibility for the ethical treatment of research participants with their collaborators, assistants, students and employees. Where a researcher comes across a psychologist carrying out research that violates the above principles the researcher should encourage the psychologist to re-evaluate the research. Source: Book 1, Unit 2, Ethics, Values and Comparative Research, ‘The Management Research Project’, The Open University Business School, p.12

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PROBLEM DEFINITION AND RESEARCH OBJECTIVES

As you will have seen from Figure 1.1 in Section 1 of this unit, the first stage of any research project is to define the research problem and establish its objectives. Driving this process may be the recognition by an organisation that it faces a marketing problem. For example: l

A garment manufacturer has been facing difficulties in selling to retailers in a particular city, though sales elsewhere have been good. He would like to know what is wrong in this city.

The general manager of a five star hotel must continuously add new facilities for the guests. She asks, â&#x20AC;&#x153;What improvements should be made?â&#x20AC;?

A publisher has recently lost many of its top editorial staff to competitors and this exodus is more than the normal attrition pattern in this type of business. The publisher would like to know what changes are to be implemented to retain the editorial staff.

These three examples are investigative in nature and can help to identify real problem situations for which the solution will help each of these managers take effective decisions. It is in this context that the role of a proper problem definition becomes important. Before any implementation of a research project it is essential that the problem should be clearly identified. Poor identification, i.e. specifying a problem too broadly or too narrowly, results in both confusion and wasted effort. A good problem definition will pave the way for the organisation to proceed in the right direction through the subsequent stages of the marketing research process. This is doubly important where you have the marketer working with a marketing research agency. Both client and agency need mutual agreement on the research problem that is to be addressed.

3.1

RATIONALE FOR A GOOD PROBLEM DEFINITION

The best way to understand the need for an appropriate problem definition is through a real life example.

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EXAMPLE 3.1 NEED FOR A GOOD PROBLEM DEFINITION Consider the seemingly straightforward request by the chairperson of an association of community merchants for a research project. The objective of this project was to reduce the propensity of residents to do their shopping in two nearby communities. The purpose of the study was to identify and evaluate different ways to increase local merchantsâ&#x20AC;&#x2122; share of shopping by residents. Further probing however revealed that the statement of the problem was at least partially inaccurate. Only late in the research process was it learned by the researcher that the chairperson was having real difficulty convincing the other local merchants that there was a serious enough outflow of local trade to warrant joint action to reverse the flow. This changed the purpose of the research. Source: Aaker, D. A., Kumar, V., Day G. S. Marketing research, Eighth edition. John Wiley and Sons, New York 2004, p. 46.

ACTIVITY 3.1 How would the research project change in the light of this information?

COMMENTARY Now the researcher would have to measure the level of retail trade outflow, in addition to finding the reasons for the outflow. This required a major change in the research design, but had the change not been made the results could have been of little value to the client.

Why do we need a good problem definition? 1 The right question must be addressed if research is to aid decision makers. A correct answer to the wrong question leads either to poor advice or to no advice. Thus a good problem definition helps the decision maker to research the right problem. 2 Very often in research we have a tendency to rationalise and defend our actions once we have embarked upon a particular research plan. The best time to review and consider alternatives is in the planning stage. If this is done unnecessary costs involved in false starts and redoing work could be avoided. Here again, a proper definition of the research problem would be very useful. 3 A good problem definition would enable the marketing manager to ask what she would like to know if the required information could be obtained without error and without cost. 26

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4 A good problem definition would enable the marketing manager to avoid the acceptance of the superficial and the obvious. A properly formulated research problem provides a sense of direction to the precise scope and purpose(s) of the study.

3.2

THE KEY ELEMENTS OF PROBLEM DEFINITION

A complete problem definition must specify: 1

Units of analysis

Time and space boundaries

Characteristics of interest

Specific environmental conditions.

Unit(s) of analysis refer to the ‘units’ in which the audience is approached e.g. individuals, households, families. Characteristics of interest are the variables that would impact the decision. The researcher would need to measure them. Time and space boundaries will specify the duration of the research study, and the geographical limits in which it will hold true. For example, an organisation might say the duration of a customer profiling study will be 12 weeks and it will be confined to a particular region or to an entire country. Some times, it may even be a global study. Environmental factors will involve the nature and degree of competition, threats of substitutions, economic factors, political climate, and government regulations. There are three crucial questions that must be put to the decision maker at the very beginning of the problem definition. l

The first question is “what decision do you face?” If there is no decision envisaged, then there is no research problem. The second question is “what are your alternatives?” If there are no alternatives to choose, then there is no research problem. The third question is “what criteria do you use to evaluate the alternatives?” If there are no criteria stipulated, then there is no research problem.

ACTIVITY 3.2 Assume that you are the Manager (Marketing Services) for brand X colour TV which is distributed on a Europe-wide basis. Over the last six months there has been a consistent and sharp decline in the absolute level of sales for this brand. Also the profitability associated with this product line is getting eroded. The Marketing Research department has been asked to identify why sales have been declining.

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Give a comprehensive problem definition for the above situation in the form of relevant questions, alternatives and hypotheses. Make sure that you incorporate the elements – unit of analysis, characteristics of interest, time and space boundaries and the environmental factors.

COMMENTARY Units of Analysis: Dealers, Distributors and Households Characteristics of Interest: Sales volume in terms of Euro value as well as physical quantity, variations in market share regionwise, segment-wise and competitor-wise, trend in demand for brand X vis à vis other brands, macro-level gap between demand and supply, percentage market growth of consumer electronic industry. Time and space boundaries: Time of completion of the diagnostic study to be specified for subsequent decision making. The space boundary – regional or national study as well as export destination need to be specified. Within a region, it could also mean one or more states/cities. Environmental factors: Government policies with regard to imports, domestic production, granting of licenses, tax levies, etc., resistance from consumer protection forums, SWOT profile of brand X versus competition and comparative advertising. Possible relevant questions that may be raised with regard to the problem are: 1

What is the size and trend of the industry volume of sales?

What has been the pattern in the market share of brand X during the last six months and six months prior to that?

Is the price of brand X higher or lower than those of the competitors?

Is the decline in sales confined to a particular segment only or across all segments?

Is it a problem of production or sales coordination?

Is it problem of logistics (transportation/distribution)?

Has there been an appreciable decline in the quality of the product and service after sales?

Is there a glaring visibility of competing brands owing to greater advertising and promotional strategies?

Possible alternatives/hypotheses: 1 The industry as a whole has been witnessing a steady decline in sales volume and so it is not situation specific to brand X alone. The probable cause could be due to a

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steep rise in duties (excise, import) or imported components, and also fluctuations in foreign exchange rates. 2 Increased pressure of competition coming from the lower priced brands. 3 Competitors wooing dealers and distributors with much better discount structures, margins and other sales incentives.

3.3

MARKETING PROBLEM AND

RESEARCH PROBLEM

The marketing manager who has to make use of marketing research as a decision support mechanism must be familiar with research procedures. This is needed in view of the following three reasons: 1 The marketing manager must implement or reject research recommendations. This necessarily compels the manager to understand the implications arising out of the interpretations of the results along with the assumptions implicit in them. 2. The marketing problem is always the prerogative of the marketing manager and they pose the initial problem and know the environment in which it exists. This clearly implies that the manager must have a good grasp of the kind of questions the research project can handle, and the kind of structure needed to make the problem “researchable”. 3. The marketing manager will need to be able to evaluate the research proposals when they are first presented, comment on initial drafts of results and perhaps raise questions about any issues that are not very clear. Finally, the manager will have to discuss the findings and their implications with colleagues within the firm. Of course, it is not just managers who are responsible for commissioning research that need to possess these skills – any senior manager may expect to receive marketing research reports and discuss them. The marketing problem set in motion by the manager is the most crucial one for decision making. The role of the researcher is to inform the decision stipulated by the manager. The marketing problem is the prerogative of the marketer and the researcher cannot simply dictate terms. On the other hand the research problem is within the scope of the market researcher to formulate (based on the marketing problem). In fact, unnecessary friction between the two could be avoided if the research problem is jointly formulated by the marketer and the researcher. The researcher’s focus will generally be on the specific issues of the entire research process. In contrast, the manager’s focus should be

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on how the results would be useful, their implications, the inputs that are needed in order to use the research methods suggested, and what insights the methods might not provide. The box below gives the example of how a research agency, Custom Research International (CRI), presented research proposals to its clients. In this example the company gives the background to the research, specifically the marketing problem facing the company i.e. that wheat prices have been rising. In order to assess the impact of this on the client’s business CRI identifies three purposes for the research.

EXAMPLE 3.2 DERIVING A RESEARCH PURPOSE Research purpose To track: (1)

consumer awareness of pricing changes in the pancake mix category

(2)

consumer perceptions for the reasons for the price increase and

(3)

expected claimed habit changes due to pancake mix pricing volatility.

Success criteria/expected results Will be used with other data to aid brand management in decisions about product, promotion, pricing and public relations responses to the pricing increases.

Background The wheat futures market is extraordinarily volatile with spring futures increasing by over 50%, the second highest level ever. ... this market is being driven by fundamental supply and demand issues as worldwide stocks of higher quality wheat are at critically low levels and estimates for this year’s crop are 10% lower than last year. We expect continued volatility over the next 3 months.

Method/Test Description In an effort to understand consumer awareness of pancake mix pricing increase and how the price increase affects consumers’ usage habits, we will conduct a series of four telephone surveys at six week intervals. Custom Research Inc. S.S. Harmeling and W.J. Bruns, (1998), Harvard Business School Case Study 9-199-001

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Examples of a connection between marketing problems and research problems Marketing Problem

Research Problem

(1) Select appropriate package design for the new product.

Evaluate the alternative package designs proposed in terms of customer preference.

(2) Should the new product be launched?

Assess the consumer reaction and acceptance to the new product through a test market.

(3) Select appropriate advertising campaign.

Evaluate alternative advertising themes in terms of effectiveness.

(4) Increase market penetration by opening new stores.

Evaluate prospective locations in terms of sales potential.

(5) Develop pricing strategy for a new product.

Assess demand potential for different price levels proposed.

(6) Increase advertising budget amount on existing product lines.

Evaluate the impact of present adversting campaign on volumes of sales.

ACTIVITY 3.3 For the following marketing problems, state the equivalent research problems: (a) Increase consumer traffic for a supermarket (b) Make a geographic allocation of an advertising budget.

COMMENTARY Which research problems did you identify. How would you compare the alternatives that are possible? Marketing Problem

Research Problem

(a) Increase consumer traffic for the supermarket.

Measure the current image level enjoyed by the supermarket as well as its traffic volume.

(b) Make a geographic allocation of the advertising budget

Estimate the current market penetration level in the respective geographical areas under consideration.

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3.4

RESEARCH OBJECTIVES

The research objectives can be identified once the research problem has been identified. For example, a supermarket would like to know “why its sales are declining”. Some typical research objectives could be stated as follows: l

To find out the customers’ perceptions with regard to the service quality of this supermarket and of the competition

To find out the proportion of advertised ‘specials’ out of stock

To assess whether the advertising needs radical changes.

Each research objective is a direct derivation from the kind of questions the research problem poses. The following table gives two examples that would clarify how a research objective is derived from the research questions.

Marketing Problem/ Question

Research Questions

Research Objective

Should the supermarket offer in-home shopping through cable television?

Are consumers aware of in-home shopping? How would the consumers react to in-home shopping via cable television?

To determine consumers’ awareness with top of the mind recall* or aided recall**. To find out the attitudes and beliefs of the consumers with regard to home shopping via cable television.

Should we reduce our price in line with the competition?

What is the brand image of our product in the marketplace? How would the consumers react if we retain the existing price?

To find out consumers’ rating of our brand and the competition. To find out whether the consumers are price sensitive.

* Top of the mind recall involves assessing whether a particular brand (e.g. Volkswagen) is the first one that comes to a respondent’s mind when asked about a particular product category (e.g. saloon cars). ** Aided recall is a research technique in which the respondent is aided in remembering his or her exposure to advertising products or media.

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ACTIVITY 3.4 Outline the research questions and the research objectives for the following marketing problem: In which of the forms (tablets, liquid or capsules) should firm X sell its drugs?

COMMENTARY Marketing Problem/ Question

Research Questions

Research Objective

In which of the forms (tablets, liquid or capsules) should firm X sell its drugs?

What are the reactions of consumers to drugs in the form of tablets, liquid or capsules? How do the consumers perceive the advantages and constraints of each form?

To find out ratings for each form (tablets, liquid or capsules). To find out the perceived advantages and constraints experienced for each form (tablets, liquid or capsules).

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4 EXPLORATORY RESEARCH

EXPLORATORY RESEARCH

In Figure 1.1, presented in the Section 1, we illustrated how, once the research objectives had been established, the research design needed to be selected. There are two broad options to choose from, exploratory designs and conclusive designs. In this section and the next we will deal with exploratory designs and, after that, from Section 6 with conclusive designs. “What will happen if we increase the price of our product by 10%?” “Would the new model we’re thinking about enhance consumers’ preference significantly?” “How can we improve our relationship with our dealers?” “Why have our sales been gradually declining?” “Should we be doing more television advertising?” The answers to all these questions involve the selection of appropriate research designs. The ‘research design’ for a marketing research project is the blueprint of how the research will be conducted. The design takes into account the strengths and limitations of the different research designs available to the researcher. Selection of the design depends on the kind of answers we can get for the decision problem envisaged, and how confident we want to be with regard to the findings of the study. However, it is important to remember that whichever design is chosen for a marketing research project, it will not make the decisions and it cannot guarantee success. This is because the variables that are usually of interest to marketing researchers rarely, if ever, lend themselves to deterministic modelling. For example, if a marketer wants to know how much to spend on promotion in order to achieve a certain market share the problem they face is that the link between promotional expenditure and sales is not so direct as between, for example, air pressure and temperature. In this section, we will describe the types of research designs, their uses, and the distinctions that exist among them. Figure 1.1 has classified the different research designs. We will start by looking at exploratory designs and within that cover secondary research (although you should note that secondary data is also used for conclusive research). In the next section we will continue with the exploratory theme but shift the focus to qualitative research. In subsequent sections we will look at conclusive designs and within that broad heading look at descriptive and causal research. You should note that the order in which we are covering these designs reflects the order in which they are often, but not always, used in research.

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4.1

EXPLORATORY RESEARCH

Exploratory research is designed to identify the decision problem and as a result bring about a better understanding of the research problem. Its main purpose is to get initial insights when the researcher is unclear about the problem and unable to define it. As we saw in the previous section, defining the problem requires the marketer to have some idea about the decision that is being faced, the alternatives and the criteria to be used when evaluating the alternatives. Exploratory research generates a number of ideas/hypotheses and gives a sense of direction to the analyst in identifying the research problem. Secondary data sources, surveys of experts, pilot studies, and qualitative research are used in this research design. To understand what exploratory research is, consider the following example:

An example of exploratory research Alpha furniture manufactures office automation and specialty equipment. It does direct selling through its sales force. In spite of a previous good track record in the industry in terms of growth and financial indicators, there has been substantial erosion in sales and profits in the last 12 months. The management of Alpha is worried about this development. In this instance the primary aim of marketing research should be to help Alpha arrest the decline in sales and profits. We cannot do this unless we are in a position to identify probable causes of the erosion in sales and profits. We certainly need a diagnostic study to identify the right problem. Is it due to poor quality of its products? Could it be due to falling motivation and morale of its sales force? Is it due to ineffective advertising and sales promotional schemes? Alpha officials must think about these questions carefully to identify the right problem for further research. This requires exploratory research where the possible problems facing the organisation are vague and the right one cannot be immediately identified. Exploratory research is less formal than some other forms of research in its approach. It is designed to provide some background, and as the word means, just “explore” the subject of interest. Exploratory research is characterised by its flexibility. It generates insights, leads and hunches. It can generate hypotheses to be tested. It does not test them and as a result it doesn’t require a large, representative sample size. Its findings are more tentative than those arising from descriptive or conclusive research and as a result are less likely to be used to formulate marketing decisions. The lack of specificity associated with the early stages of exploratory research and the fact that researchers start off admitting that they are not entirely sure what they are looking for can actually be seen as a strength. This is because exploratory research can generate findings that managers did not know were possible. 36

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The methods associated with exploratory research can help. For example, in-depth interviews are supposed to make use of open-ended questions – and these can leave respondents with a lot of freedom to talk about issues that are most important to them. As a result, they can come up with unexpected answers. The box below gives an example of an exploratory question used in research.

BANK CARDS IN CHINA Card holders were asked why they used bank payment cards with the following question: “what was the purpose(s) of having a bank card(s)?”. The answers ranged as follows: I would like to use my card to withdraw money from ATMs [cash machines] everywhere and pay for everything with cash... However another answer revealed much about the culture in China and the decision-making processes within households: “I give all my salary to my wife and she gives me some cash to cover daily expenses. Therefore, I depend on my bank card, if any expenses occur.” An understanding of why consumers hold and subsequently use payment cards is fundamental to the successful marketing of these cards. Knowing who holds the purse strings in a family unit and what aspirations consumers have, should help marketers segment and then target the market(s) more effectively. Worhington, S. (2003) ‘The Chinese payment card market: an exploratory study’, International Journal of Bank Marketing (pp. 324-334)

Exploratory research clearly shares some of the characteristics of qualitative research. However, it would be wrong to think that exploratory research is only ever qualitative. Within the heading of exploratory research we also have secondary research and this can clearly involve reference to quantitative data (e.g. published government statistics). Sometimes such data will only give tentative insights into the areas in which the marketer is interested, since the data will most likely refer to situations that are only broadly similar to the focus of the study. For example, published statistics on consumer expenditure may contain a range of categories, while the television manufacturer undertaking the research is only interested in expenditure on television sets.

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ACTIVITY 4.1 In which sorts of circumstances do you feel exploratory research can be undertaken? And should it always accompany conclusive research?

COMMENTARY Exploratory research should be undertaken where there is not enough information to undertake a conclusive study. However, there can be situations where managers only undertake the exploratory stage of research and do not continue any further. They may have only very limited time and/or financial resources. Furthermore, the nature of the research may preclude usage of techniques that are associated with conclusive research, for example the use of samples representative of a wider population. It is relevant to point out here the distinctions between exploratory research and conclusive research. In conclusive research, the aim is to test specific hypotheses and examine relationships. Conclusive research is highly structured and more formal than exploratory research. Distinctions between exploratory and conclusive research Exploratory Research

Conclusive Research

Aim

To provide insights, hunches, and understanding.

To test stipulated hypotheses and relationships.

Distinctiveness

Information needs are defined rather loosely. Flexible and unstructured in approach. Sample size is small and non-representative Data analysis is mostly qualitative. Such analysis is less to do with â&#x20AC;&#x2DC;countingâ&#x20AC;&#x2122; the frequency of observations than trying to find patterns of meaning.

Information needs are well articulated and defined clearly. Formal and structured in approach.

Results

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Tentative. Generally followed by conclusive research.

Sample size is large and representative Data analysis is quantitative

Final and confirmatory. Direct input into marketing decisions.

4 EXPLORATORY RESEARCH

Methods for conducting exploratory research Secondary data Secondary data, as we know from Section 2, refer to data that have already been collected. In contrast, primary data are collected by the organisation itself, for its own specific purpose. The benefits of primary data are that they fit the needs exactly, are up-to-date and reliable. Secondary data have previously been collected by other organisations or for other purposes. Any data which are not collected by the organisation for the specified purpose are secondary data. These may be published by other organisations, available from research studies, syndicated services, published by the government, and so on. Secondary data have the advantages of being much cheaper and faster to collect than primary data collection methods. By carefully analysing secondary data that are relevant to the marketing research project, we can get useful insights that will lay the foundation for conclusive research. Sometimes the inferences drawn through secondary data might totally eliminate the need for doing conclusive research!

Applications of secondary data We have just discussed the nature of secondary data and the sources where it can be found. Here we will look at the way in which secondary data can be used. A key use of secondary data is to estimate demand. For example, industry-wide statistics can give some indication of the size of the total market. The annual financial reports of competitors can give some idea of their sales. Secondary data can also be a useful means of monitoring the marketing environment. Such information will give the marketer an insight into the latest developments taking place at the level of both the micro and macro environment. David Aaker, George Day and V. Kumar also refer to the use marketers can make of secondary data to segment and target their customers. For example, a product called â&#x20AC;&#x2DC;PRIZMâ&#x20AC;&#x2122; (Potential Rating Index Zip Markets) links U.S. census data on the basis of postal code to lifestyles. Using this information, marketers can identify which areas they would like to focus their attention. The assumption underlying this particular database is that people with similar cultural backgrounds and purchasing behaviours will tend to live close to each other. In his book, Competitive Intelligence, Conor Vibert gives some ideas as to how different sources of information can be used to gain an insight into competitors. He refers specifically to the 10-K and 10-Q filings made by US corporations. (You should note that

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publicly listed companies in other countries may have different disclosure requirements.) He points out that filings made by US companies can be used to gather information about a firmâ&#x20AC;&#x2122;s financial performance, a review of product and service offerings, information about mergers and acquisitions, details of the geographic markets in which a firm operates, information about outstanding legal actions against a company and the companyâ&#x20AC;&#x2122;s market risk factors. The latter, in particular, is information that is not generally disclosed by companies listed in the UK. In the box below we reproduce an extract from a 10-Q statement published by Therasense. This company produces blood glucose monitors. The extract identifies the risks that companies in this sector can face.

THERASENSE 10-Q STATEMENT The risks and uncertainties that may affect the operations, performance, development, and results of our businesses include but are not limited to (1) our limited operating experience and history of losses; (2) limited manufacturing experience; (3) our dependence on FreeStyle for future revenues; (4) substantial competition; (5) risks related to non-compliance with FDA regulations; (6) risks related to failure to protect our intellectual property and litigation in which we may become involved; (7) our limited sales and marketing experience; (8) our dependence on single source suppliers and manufacturers for our FreeStyle products; and (9) other factors that are described from time to time in our periodic filings with the Securities and Exchange Commission, including those set forth in this filing as "Factors Affecting Operations and Future Results". http://primezone.finsys.com/edgar_conv_html%5C2001%5C11%5C14% 5C0001032210-01-501377.html

Conor Vibert also argues that although government agencies may not be competing in the commercial sense of the term, they may also need to use competitive intelligence. This is because they may be competing with other jurisdictions and countries or other government departments for public support for their activities, and in order to attract and retain staff. Competitive intelligence can provide insights into the following areas: best practices in government service delivery; developing public policy in the context of a global economy; developing legislation and regulation.

ACTIVITY 4.2 What do you think can be possible problems associated with using secondary sources of information? What possible solution can you think of?

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COMMENTARY The following box refers specifically to using secondary sources of information about China, but these weaknesses can apply elsewhere as well.

Data published on production, stocks, consumption quantities, and especially on the money values and indexes of such data, need to be treated critically. Much of this data, be it from the government itself, organisations, or research companies in China, is governmental in origin. China’s government is vertically organised, so each industry’s ministry or department collects its own information. Often, various departments collect statistics on the same topic using different, often flawed, methods and scopes (for example, different series collected by various agencies can define administrative regions, time spans, and commodities differently). When researchers use secondary sources it is imperative that they compare multiple sources and include long time series – because it is harder to distort information over long time periods. For secondary sources in China to gain credence, the sources should reveal methodology and definitions. On many occasions, wild discrepancies in data series make sense after the primary source data collectors explain their definitions and methods. One difference between China and more developed markets is that, in China, crosschecking and triangulation are essential in designing the research agenda. Simply asking the same question two ways at different points in the interview can signal the value of information being given. Relying purely on one perspective – no matter how many like-minded people say the same thing – can result in significant biases, so getting different stakeholders to comment on an issue can help pin down any hidden information. Charles Oliver and John Coulter, ‘China market research strategies’, China Business Review, May-June, 2004 pp. 54–59

Evaluating secondary sources Although secondary information is relatively easy and cheap to obtain, before marketers make use of it, they need to be aware of the possible limitations. An assessment needs to be made of the organisation authoring or sponsoring the study that is being cited. There are issues to do with competence (do they have the ability and resources to undertake the type of study that has been written) and also motivation (does the organisation have an agenda that it wishes to pursue?) Users of secondary information also need to have some awareness of how the data was gathered – this may give some indication of underlying biases in the information. Depending on the topic being OU BUSINESS SCHOOL

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considered, the age of the information may have a very important impact on its usefulness. There can also be what are referred to as ‘problems of fit’. The classifications used in the secondary data being analysed may not fit the classifications of interest to the marketer. For example, the marketer may be interested in the buying habits of children aged 10–12, while available data may only cover age ranges 7–10 and 11–15. Richard Jurek points out that the quality of secondary research can be improved if the following steps are taken. It is amazing how often people in both large and small organisations cite statistics in management reports or presentations without even looking at a study’s design or comparing contrary findings. One of the most valuable steps here is contacting the original study’s authors if possible. Few researchers actually do this; those who do, though, generally find that they are well rewarded ... one often gains access to additional findings.

Another method of improving the quality of the information that is used is to ‘triangulate’ the sources of information. Two different sources using different methods who arrive at similar results will be more credible than a single source on its own. Jurek distinguishes between appropriate and inappropriate uses of marketing research as follows. He says appropriate uses are: business/competitive, intelligence/strategic trending, brainstorming, primary study idea generation; factoid style material (food for thought). Inappropriate uses are: decision making; inference asserting; conclusion validation.

ACTIVITY 4.3 Do you agree with the view “There is no benefit in generating primary data if satisfactory secondary data are available”?

COMMENTARY Gathering primary data can be more costly than secondary data in terms of both time and money. If the secondary data that could be obtained from government agencies, academic institutions, trade associations, or a wide variety of other sources are of acceptable quality at a lower cost, it is appropriate to use these alternatives. Quite often the data provided by such organisations are better than the data a small private firm could acquire if it were to do its own primary research.

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Internal secondary data Data that are generated within the firm and are (or were) collected for some purpose other than the current research, constitute what is called internal secondary data. Sales and cost data from management accounting reports are prime sources of valuable data. Other sources include data on marketing activity inputs, cost information, distributor reports and feedback and customer feedback that is collected on an ongoing basis (for example, completed feedback forms). These are available on a wide variety of bases; for example, geographic divisions, products, types of customers, departments and channels of distribution. The availability of both sales and cost data enables the researcher to identify and focus on profitable items.

Caution The researcher should not accept these data at face value any more than he or she should accept other data without careful scrutiny. The assumptions made in assigning costs to products, departments and time periods must be critically evaluated in terms of sound methodology before the data are accepted. Reporting formats used for the collection of secondary data may be inappropriate for the needs of the marketing department â&#x20AC;&#x201C; for example, sales data used for accounting purposes may not show sales by region or type of customer, and manipulating the data so that it does so may be time consuming and expensive. In addition, users of such data need to make sure that the way in which the data was collected was free from bias. For example, sales data may not accurately reflect the real level of sales if the company has a liberal return-of-goods policy. Although customer feedback in the form of questionnaires lends itself to quantitative analysis, ad hoc letters of complaint can be useful as well. These can help identify the needs of customers with unusual needs. Of course, marketers need to be aware that complaint letters need not necessarily reflect wider dissatisfaction. Attention therefore needs to be paid to who is complaining and whether the source of this complaint is likely to affect wider groups of customers. As with other types of exploratory data this source tells us what exists, but it does not tell us to what extent. In order to assess that, more conclusive research would need to be undertaken. Internal secondary data have two major advantages: (1) availability and (2) low cost. Researchers should fully familiarise themselves with the kinds of internal data and reports available.

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USE OF INTERNAL SECONDARY DATA: SAMSONITE Samsonite Corporation uses internal secondary data with a view to building a good customer relationship that will lead to higher satisfaction level. The ultimate aim is to get an excellent “travel expert” image that will pave the way for the company to sell products directly to customers. The information content emanates from the consumer response cards and also from an 800-number [free phone] Consumer Link program. The information is further analysed to throw light on advertising effectiveness, media effectiveness, caller characteristics, call-to-purchase delay, and the pattern of purchases made by the customers with regard to specific types of luggage. The analysis also helps Samsonite undertake new product planning and modifications of existing products. Incidentally, Samsonite judiciously used this type of secondary data analysis to find out that the repurchase intent was in the mid–90s for the Piggyback Carry–On(PBCO) one of the highest scores ever obtained by the company. Source: Adapted from Robert Bengen, ‘Teamwork: It’s in the Bag’, Marketing Research: A Magazine of Management and Applications, 5 (Winter 1993)

External secondary d ata There are a wide range of external secondary data sources available. However, given the pace of change in information today, we will cover the different sources of information available on the B825 website. The box below shows one organisation that supplies secondary data.

GESIS – GERMAN SOCIAL SCIENCE INFRASTRUCTURE DERVICES E.V. GESIS provides services in support of social science research, including the development and supply of databases with information on social science literature and research activities, as well as the archiving and provision of survey data from social research. Services including consultation on methodological questions, the development of complex methods of empirical social research as well as GESIS’ own long-term observation of social developments with the aid of these instruments, are also available. http://www.gesis.org/en/index.htm

Syndicated services Several organisations collect data on various aspects of marketing and associated consumer behaviour. This systematised information is sold on a subscription basis to clients who are in need of it. It 44

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is definitely more expensive than the published secondary data discussed above, but it is typically much less expensive than primary data. The firms offering these services have the advantage of spreading the cost over a large number of clients. The data may not uniquely fit into the specific needs of individual clients, but it may be possible for them to ‘piggyback’ specific questions on the general format by paying an additional fee. ��

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5 EXPLORATORY RESEARCH â&#x20AC;&#x201C; QUALITATIVE RESEARCH

EXPLORATORY RESEARCH â&#x20AC;&#x201C; QUALITATIVE RESEARCH

As you will recall from Figure 1.1 in Section 1, exploratory research can be further divided into secondary research and qualitative research. In the previous section we looked at exploratory research and focused on the use of secondary sources of information. Here we will continue the discussion on exploratory research and deal with qualitative methods of gathering data and information. Since data will be collected specifically for a particular marketing research project, they are classified as primary data. Qualitative research focuses on collection, analysis and interpretation of data that cannot be put in the form of numbers. Typically, qualitative research involves relatively unstructured questioning with relatively few respondents participating in the study, in contrast to quantitative research which is associated with large sample sizes. The key difference between the two types of research is the nature of the information that they provide and the use to which it can be put. The main purpose of qualitative research is to get useful insights, ideas and opinions from respondents in their own words. These can be used to lay the foundation for a better problem definition of the research study. Of course, there are many research projects that only ever use a qualitative approach, i.e. they do not get beyond the exploratory stage. This is because the sample size is very restricted or, for example, the results of the study are required very quickly and so on. Jon Chandler and Mike Owen explain the difference as follows: Qualitative researchers adapt and adjust the approach as they

proceed in order to respond to and explore what emerges, as it

emerges. In ideal qualitative work, what structure there is, is

designed to help things emerge. The ideal is that structure should

never be applied too rigidly as it will inhibit or constraint what

comes out. Fluidity and responsiveness are key aspects of the

qualitative approach. Here there is a clear contrast with the more

fixed character of quantitative research which poses precisely

structured questions in order to generate a more numeric result.

Developing brands with qualitative research, 2002, Sage, p.59

In this section we will cover some of the methods, such as case studies and in-depth interviews that can be used to gather qualitative data. We will subsequently deal with some of the broader technical issues.

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Gerald Zaltman describes the unique insights afforded by various qualitative techniques as follows: ‘Marketers assume that consumers can easily describe their own emotions. In fact, emotions are unconscious. In order to elicit them researchers need to use special probing techniques. For example, when asked why they purchase an expensive brand of chocolate, people may immediately say that they have done so as a gift for someone else. However, in reality, the purchase may have been for themselves, with the reasons involving guilt and joy lying beyond [their] normal conscious inspection. Fixed response questions, in particular, won’t get at consumers’ most important thoughts and feelings if the manager or researcher has not first identified them by penetrating consumers’ unconscious thought’. Zaltman, G. (2003) How customers think, Harvard Business School Press, ISBN 1-57851-826-1

Zaltman’s phrase ‘fixed response questions’ refers to the types of questions used in quantitative studies that use questionnaires where respondents can only choose between answers already printed on the questionnaire. Such questions are also referred to as being ‘structured’. That approach allows for the measurement of data, but may not necessarily allow the respondents to say what they really think – since the alternatives on the questionnaire chosen by the researcher may not always relate to the respondents’ feelings. Before looking at ways in which qualitative data can be gathered, we highlight in Table 5.1 other important differences between qualitative and quantitative research. Table 5.1

Qualitative research

Quantitative research

Objective

To gain good insights into motivations, underlying reasons and understanding the customer wants.

To quantify the market data, analyse it, and draw conclusions about the population parameters based on the sample.

Sampling plan

Small sample size that is based on non-random sampling and un representative of the population.

Large sample size that is based on random sampling and representative of the population.

Data Collection

Unstructured and loosely defined.

Structured and involves formally defined procedures.

Data analysis

Non-statistical and involves subjective interpretations.

Highly statistical and involves objective interpretations based on quantitative data.

Overall result

Just provides an initial understanding, hunches and insights. Diagnostic and exploratory in nature. Further studies may be needed before making a final decision.

Makes a set of actionable recommendation that are direct input into the marketing decisions.

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ACTIVITY 5.1 A large hospital would like to assess the patients’ perceptions about the services it offers. Give a brief outline as to how you would conduct qualitative and quantitative research for this study.

COMMENTARY Qualitative research The hospital administrator could develop a feel for the nature and degree of anxieties encountered by the patients when they visit the hospital on dimensions such as politeness, empathy, and courtesy of the staff towards the patients. The hospital could get valuable inputs by talking to a selected few customers informally, in an unstructured manner, as to what they think about the service quality provided. It could also conduct a focus group discussion with a set of say 6 to 8 patients with the help of an expert moderator to get a fairly good idea about the perceptions of the patients. Quantitative research The hospital administrator could ascertain the patients’ perceptions using a quantitative rating scale on important attributes such as food quality, room service and treatment given. The data collected from a well-defined and reasonably large sample of respondents could be analysed to implement meaningful decisions for the hospital. The nature of the analysis will be statistical. Before we end the comparison of qualitative and quantitative research we will address the issue of ‘quality’ and the different approaches that are used to assess it. Quantitative research methods, such as the use of sample surveys, allow the researcher to identify with a given level of certainty the answers that would come from a whole population by asking questions of just a selected or sampled minority. Reliability in a survey means the ability of the methodology to produce the same results if repeated. The parallel goal of validity relates to the equally key requirement that we are actually measuring what we set out to measure. Jon Chandler and Mike Owen say the following: ‘Qualitative research, as it is practised in Europe, almost by definition fails to achieve some of the core requirements of these... views of social science. Qualitative research rarely sets up anything resembling a formal experiment conducted under controlled conditions which can be precisely repeated by others if they wish. The qualitative approach is at best ‘wobbly’ on issues to do with reliability. Few practitioners would ever claim to, or want to, exactly replicate or imitate the approaches of others. Again,

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virtually all qualitative researchers would say that a single interview is a unique and unrepeatable event. Qualitative research operates to a different set of rules...it does not offer reliability...it addresses the fundamentally problematic issue of validity...that we are examining what we think we are examining,’ Chandler and Owen 2002

The above requires that the respondent in a qualitative piece of research understands the questions that are asked and interprets them as intended. The respondent also has access to the truth and the ability to provide this in their response. The researcher must also understand the response as it was intended.

5.1

CONDUCTING QUALITATIVE RESEARCH

Table 5.2 below identifies the key differences between using focus groups and individual in-depth interviews to gather data. For each of these differences the table shows how these methods of gathering data vary in terms of their advantages and disadvantages.

5.2

FOCUS GROUPS

Focus group interview (or focus group discussion) involves an objective moderator who introduces the topic for discussion to the participants and conducts the entire meeting in an unstructured and natural fashion. Sessions normally last from one to two hours.

Focus groups and ethnographic research Focus groups are liked by marketers because they are cheap, quick and easy, as all you need is a moderator, 6–10 people, a topic and a two way mirror. However, the way in which they are used can be problematic. People cannot easily explain their thoughts, actions and emotions in words, especially in a group setting. Procter & Gamble (P&G) had a packaging team observe a consumer doing her laundry. In a focus group she had previously said that she had had no problems doing her laundry. However, they saw her use a screwdriver to open the new box of detergent and poke a stick into the wash so that the driver dissolved. When asked about these actions, she said, “I didn’t think about them”.

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Table 5.2 Focus Groups

Individual In-Depth Interviews

Group interactions

Group interaction is present. This may stimulate new thoughts from respondents.

There is no group interaction. Therefore, stimulation for new ideas from respondents comes from the interviewer.

Group/peer pressure

Group pressure and stimulation may clarify and challenge thinking.

In the absence of group pressure, the thinking of respondents is not challenged.

Peer pressure and role playing may occur and may be confusing to interpret.

With one respondent, role playing is minimised and there is no peer pressure.

Respondent competition

Respondents compete with one another for time to talk. There is less time to obtain in-depth details from each participant.

The individual is alone with the interviewer and can express thoughts in a non-competitive environment. There is more time to obtain detailed information.

Influence

Responses in a group may be “contaminated” by opinions of other group members.

With one respondent, there is no potential for influence from other respondents.

Subject sensitivity

If the subject is sensitive, respondents may be hesitant to talk freely in the presence of several other people.

If the subject is sensitive, respondents may be more likely to talk.

Interviewer fatigue

One interviewer can easily conduct several group sessions on one topic without becoming fatigued or bored.

Interviewer fatigue and boredom are problems when many individual interviews are needed.

Amount of information

A relatively large amount of information can be obtained in a short period of time at relatively small cost.

A large amount of information can be obtained, but it takes time to obtain it and to analyse the results. Thus, costs are relatively high.

Stimuli

The volume of stimulus materials that can be used is somewhat limited.

A fairly large amount of stimulus material can be used.

Interviewer schedule

It may be difficult to assemble 8 or 10 respondents if they are a difficult type to recruit (such as very busy executives).

Individual interviews are easier to schedule.

Focus groups take a short amount of time and are undertaken in artificial settings. Ethnography on the other hand does not look for opinions rather a more in-depth understanding of how a product might ‘resonate with a consumer’s daily life.’ The P&G example described above illustrates this. Another example is of Whirlpool, the manufacturer of luxury jetted bath tubs. They undertook research to see how consumers reacted to their products. Researchers visited consumers in their homes and filmed participants while they soaked. In addition, consumers were asked

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to create a journal of images which represented what they thought about when they thought of their tub. Ethnography is based on finding out what is important to customers. The ideas come from the consumers themselves, rather than having them respond to questions. Focus groups can therefore be best used to: design other types of research and interpret the results of other research. In the latter instance focus groups are used not to draw conclusions but to understand the conclusions drawn.

5.3

IN-DEPTH INTERVIEWS

An in-depth interview involves a personal interview in which a single participant is probed by a highly skilful interviewer in order to identify underlying motivations, beliefs, insights and feelings on a particular topic of interest. The interview is conducted in an unstructured and natural fashion. Thus the difference between focus groups and in-depth interviews is that, for in-depth interviews, the discussion takes place on a one-to-one basis. Typically an in-depth interview may take 30 minutes to 1 hour to conduct. The interviewer encourages the participant to talk freely on his/her attitudes towards the topic that is being discussed. Probing is the key for uncovering hidden issues in the mind of the participant.

ACTIVITY 5.2 When are in-depth interviews preferred over focus groups?

COMMENTARY In-depth interviews will be superior to focus groups where indepth probing of the respondent is needed, for example: (1) Confidential, delicate, and sensitive areas such as personal finance. (2) Obtaining data from competitors (3) In industrial marketing where in-depth interview is much more effective than the focus group. You should look at the section titled â&#x20AC;&#x2DC;Identifying the core valuesâ&#x20AC;&#x2122; in Unit 5 of this course. That unit discusses how qualitative techniques are used within organisations to elicit their brand values.

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5.4

PROJECTIVE TECHNIQUES

Both focus group discussions and in-depth interviews are conducted in an unstructured and natural fashion where the purpose of the study is undisguised. The moment we disguise the exact purpose of the study, it takes some form of a projective technique. Of course there are ethical issues related to this, but they would be related to the extent to which the study was disguised and the impact on the respondent. Instead of being direct, projective techniques are conducted to encourage the respondents and project their motivations, beliefs and attitudes with regard to the issues of concern. In other words, a formal definition of projective technique will look like the following: A projective technique is an unstructured, indirect way of questioning that actually encourages the respondents to project their own behaviour, motivations, insights into buying, beliefs and attitudes regarding the issues of concern. The interesting thing is, in projective techniques, the participants are asked to interpret the behaviour of others rather then to describe their own behaviour. In interpreting other people’s behaviour the respondents in a way project their own behaviour.

Types of projective techniques Several types of projective techniques are used in marketing research. Among them are the Third Person Technique, Sentence Completion, Word Association, Thematic Apperception Tests (TAT) and Story Completion. We’ll deal with a couple of these to give you an idea of how they can be used. The use of projective techniques assumes that the respondents will ‘project’ themselves into the situation presented. If they answer for others without projecting themselves into the situation, projective techniques will fail. The essence of projective technique is, if the researcher believes the respondent is either unable or unwilling to supply the requested information, the researcher changes from a direct to an indirect approach by disguising the purpose of the study.

Third person technique The simplest projective method is the third-person technique. Respondents are asked to describe behaviour, attitudes or motivations of their neighbours, friends or people in general. Respondents can ‘project’ their own answers to others without subjecting themselves to the constraints of social acceptability. Delicate topics such as personal hygiene and racial prejudice can be investigated through this technique. This technique is also useful in situations in which respondents are unaware of their own feelings. They may refuse, and perhaps be unable, to respond honestly. If asked to analyse and explain the actions of third parties, they can easily ‘project’ their hitherto unrecognised responses onto others without feeling threatened.

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Examples The third-person technique is apt for discovering buying motives. The respondent may be asked to explain the probable reason a friend would purchase a particular product or patronise a particular store. Or a hypothetical situation may be presented, for example: “If a friend of yours were trying to impress a business client, what restaurant do you think your friend would choose?”

Sentence completion questions Sentence completion questions ask the respondent to finish sentences that are incomplete. The usual method followed in practice is to instruct the participant to respond with his or her first thought.

Examples of sentence completion questions “Colgate appeals to people who ...........”

“Our local school board should ...........”

“A person who drives a Buick is..........”

“The typical shopper at Sears is........”

Responses to these questions may be vastly divergent. No single dimension is identified. The respondent fills in his or her own framework in the incomplete part of the sentence. The real advantage of sentence completion question lies in the absence of a ‘forced fit’. The researcher does not force the respondent into a framework that is at variance with the respondent’s thought pattern. Its principal disadvantage is the difficulty experienced in summarising the data. Are “be more careful of the budget” and “spend less money” two different categories or the same? The objective with sentence completion is to secure responses unfettered by role-playing.

Advantages and disadvantages of projective techniques Because the purpose of the study is disguised, projective techniques can elicit responses that a participant would be unwilling or unable to give if they knew the purpose of the study beforehand. This is indeed a major advantage that a project technique has over focus group discussions and in-depth interviews. If the discussion topic envisages issues that are highly personal, sensitive, delicate and embarrassing, projective techniques are the only way out. Projective techniques have many of the disadvantages of focus group discussions and in-depth interviews but to a greater degree. Projective techniques require highly trained interviewers who are

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competent and possess knowledge in psychology and behavioural sciences. Interpretation of the projective technique findings is very complex and highly subjective.

Application of projective techniques Projective techniques are less frequently used than focus group interviews and in-depth interviews. They can be used to measure attitude about particular products, brands and advertisements, especially when there is a need to disguise the purpose of the study. The following guidelines are recommended when considering the use of projective techniques. 1

The required information can’t be obtained by direct methods (focus group and in-depth interviews)

Use for exploratory studies to gain initial understanding and insights

Because of the complexity involved in projective techniques, they shouldn’t be used naively.

ACTIVITY 5.3 Analyse, criticise, and explain the following statement: “Direct questioning on certain subjects will be of little value”.

COMMENTARY Sometimes, it is counterproductive to tell the respondent what we are really investigating. Attitudes and other behavioural patterns don’t have the complete sanction of society. The person may be unwilling or unable to provide answers to the direct questions. All these pose major data collection difficulties. Hence, projective technique, an indirect way of questioning the respondents, assumes the paramount importance of obtaining insights into behaviour and motivations.

5.5

IMPLICATIONS OF THE INTERNET ON QUALITATIVE DATA COLLECTION

In this section we have seen how various methods of gathering qualitative data can be used. The growing role of the web (internet) allows researchers to use various online tools in order to collect data. These tools have advantages and disadvantages over traditional, face-to-face methods of gathering data. The following activity explores these in more detail.

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ACTIVITY 5.4 Assess the advantages and disadvantages of using the web for qualitative research. Bear in mind that the methods being considered here include, for example, focus groups conducted via online chat rooms and bulletin boards (instead of meeting face-to-face).

COMMENTARY l

web-based qualitative research can provide greater reach – though it will exclude the non-IT literate and those with no access to the net the cost savings are not as great as anticipated – but travelling time is much reduced for the researcher and participants

for real time methods, participants' comments are far briefer (though some suggest they contain the same amount of content but are simply more concise)

in using bulletin boards, people are more reflective

people can express emotion through capital letters, exclamation marks, etc

the transcripts are not ideal, especially for real time groups using online chat rooms: the time lag for comments appearing on the computer screen means that it can be tricky following a discussion

older participants are less likely to feel comfortable with web-based research – but young adults feel very at home with it

web-based moderating is different to face-to-face moderating: don’t assume that someone with a track record in one will be good at the other.

Expert opinion survey and lead user surveys Expert opinion surveys focus on people who are considered to be experts, if not at least knowledgeable in the particular area. By conducting interviews with them, we can get meaningful ideas and insights about the research project and be in a position to postulate proper hypotheses that could be tested later on by conclusive research An expert opinion survey, while flexible in its approach, is highly subjective. In order to get good information, it is extremely important to get quality respondents who are knowledgeable in the areas of expertise related to the decision problem of interest. Just like any other secondary data source, an expert opinion survey can be effective in studies where the market researcher and decision maker feel the need for defining the problem clearly.

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ACTIVITY 5.5 A company which is a leader in home appliances suddenly finds itself losing significant market share in microwave ovens. You are called upon to identify the knowledgeable individuals who will be able to provide useful insights for conducting exploratory research.

COMMENTARY The individuals who will be knowledgeable about the home appliances, particularly the microwave oven, will include the managers of the company who are looking after the key accounts in wholesale and retail outlets, the quality assurance manager, and the consumers who have the experience of using different brands of microwave oven.

The value of expert opinions can vary depending on the country in which the marketer is operating. The box below highlights this.

EXPERT OPINIONS IN CHINA China’s academic institutions, industry associations, and even state-owned enterprises and government offices contain an army of well-qualified experts who can offer advice and insights on market trends, regulations, and approaches to specific projects. Many are facing retirement and are more than willing to contribute for a modest fee. These experts can help design research programmes, point out good strategies, and find errors. Given China’s traditional respect for seniority, working with such experts can facilitate research access that would be more difficult otherwise. The danger in using experts, however, is that sometimes the most venerable are out of touch with the latest technology and approaches to social issues. They may assume a dogmatic, didactic, and inflexible approach that does not suit genuinely openended market research. Also, many experts cannot provide good quantitative information on a market. For these reasons, market research in China should be based less heavily on expert opinion than in developed countries. Caution, and trial attempts to involve an expert under consideration in non-sensitive, non-core tasks, may reveal strengths and weaknesses that a resume [summary] and interview cannot. In one commissioned research project, for example, the international client was mystified at abnormalities in supply and demand volumes of a key input. One of the researchers had a former colleague in the research project’s target city. The researcher made a courtesy call to the former colleague’s aunt,

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who was a production manager in a factory making the product in question (a bulk, generic herbicide). The aunt delivered the basic formula revealing a substitute raw material that was banned, dangerous, and cheap. The researchers then refocused their inquiries, with some subtlety, to quantify the extent of the problem. The right guanxi [relationship] can be a big help, but it is no substitute for systematic research.

As well as eliciting data from experts, marketers can also try and work with ‘lead users’. These are groups of customers who have an advanced understanding of the product and are experts in its use. According to Alan Ulwick there are specific problems of listening to ‘lead users’. This is because lead users are not average users – the products which emerge from their recommendations may have limited appeal. For example, a company making surgical products introduced a set of instruments that could rotate and move in many directions, based on the recommendations of lead-user surgeons. However, ordinary surgeons found the products to be too difficult to use. Ulwick (2002) recommends an approach which is designed to address this problem. He focuses on the results that customers would like to achieve with products. He gives the example of Cordis, a company that produces balloons used in angioplasty procedures, used to open up heart patients’ arteries. Interviews with cardiologists, nurses and other laboratory personnel focused not on the features that professionals wanted, but on the outcomes they wanted to achieve, before, during, and after the surgery. The company interviewed three groups of people, cardiologists (who perform the procedure), nurses who assist in the procedure and hospital administrators who focus on financial issues. The approach deliberately chose a diverse set of individuals in order to capture a range of opinions. This meant that they included both those cardiologists who underook a large number of procedures per month and those who did only a few. The task of the moderator conducting the interviews was to weed out the anecdotes, vague statements and so on. The focus of the moderator’s task was to clarify statements that were made and make sure that participants considered every aspect of the process or activity that they undertook. Where customers talked about anything that sounded like a solution, the moderator would refocus the respondents’ attention on the underlying process. Questions that the moderator asked included, for example, how the procedure would ideally be performed (barring any technological limitations) and the difficulties that they typically encountered in performing the procedure.

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Case study method This method has been defined by Kumar, Aaker and Day as: a comprehensive description and analysis of a single situation. The data for a case study usually are obtained from a series of lengthy unstructured interviews with a number of people involved in the situation, perhaps combined with available secondary and internal data sources.

The case study method involves an in-depth examination of the relevant unit(s) of analysis. The unit could be a customer, a consumer, retail outlet, salespersons, companies and so on. It can provide a wealth of information about our subjects on various characteristics. The main idea of the case study method is to gather significant details when we don’t know what exactly we want. The case study approach focuses on analysis of specific examples in order to extract fresh and possibly divergent views. The appropriate method that should be used in a case study depends upon the imagination and creativity of the person undertaking the case study and their ability to work through stimulating examples. There are no standard categories or set rules within which the case study methodology should fall. In fact, the person who is undertaking the case study method will not be able to get any useful insights and benefits if she/he follows routine procedures.

Example of case study method A large supermarket chain, with more than 100 branches spread across the country, has been making substantial progress over many years in sales and profitability. It has been able to maintain an average profitability and sales that is well above the industry level. The senior management of the supermarket wants to identify key factors that are critical for its success and it wants to take full advantage of these factors. In this example, there doesn’t seem to be a clear goal and the supermarket is unclear as to what exactly it is looking for. Each branch of the supermarket can be viewed as a case study. Of course, we may not be in a position to examine all the branches due to paucity of time and the complexity of the task involved. Instead, we can pick up as case studies two best performing branches and two worst performing branches based on critical dimensions. These dimensions will typically include the size of the branches, lay out, product lines, morale of people and the characteristics of the area in which the branch is located. With the imagination and creativity of the investigator, it is possible to sift useful insights from the two best and the two worst branches. In other words, what kind of similarities and dissimilarities that exist between the worst performers and best performers are to be studied by the investigator in order to learn about factors that are critical to improving growth and profitability of the supermarket. The case study could throw light on the degree OU BUSINESS SCHOOL

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of association between the critical factors identified and the performance of the branches. This would pave the way for the overall improvement of all the branches of the supermarket. This example illustrates the real use of a case study approach in terms of a qualitative analysis of data that involves many comparisons and contrasts. It envisages an investigator who is capable of being circumspect enough to observe minute differences across the case studies, as well as the ability to identify relationships among factors within each case study.

Observation methods In this section we will also look at observational methods for gathering data, although these can be used as either qualitative or quantitative data collection methods, depending on how they are used. Observation allows marketers to gather data on current behaviour. There are various individual methods associated with observation. For example, within exploratory research, managers can observe variables such as competitors’ pricing and promotional activities. Another important use of observational data is the collection of behavioural data, such as customers’ traffic patterns inside retail stores. This information can be collected on video and it can be used to design the layout of store displays. For certain groups of customers such as children, who are too young to explain their opinions, observational data may be the only effective method possible.

OBSERVATION: A CUSTOMER’S HIDDEN NEEDS In recent years several of the leading market research companies have hired anthropologists who observe customers on many levels simultaneously, and do not limit themselves to collecting information and ideas through questions. They are able to pick up clues on customers’ hidden needs through observing body language, spatial signals and other subtle gestures, all of which are easily missed by amateurs. Technology such as micro video cameras is making observation easier. We are currently working in Germany with one of Robert Bosch’s business units which designs and manufactures production line equipment. Through close observation of operators working in their customers’ factories, Bosch has gained much deeper insights into product requirements than from interviews with production managers. Keith Goffin and Fred Lemke, ‘Uncovering your customer’s hidden needs’, European Business Forum, Issue 18, Summer 2004 pp. 45–47

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The limitation with observation studies is that they cannot observe motives, attitudes or intentions. Although an individual’s observed behaviour clearly reflects their motives etc., there may be other factors at play as well. For example, people may be constrained by their budgets or the convenience of buying from a particular store. Observational methods may yield biased results if there is significant observer subjectivity involved. A new development in observation research is the use of mystery shopping.

MYSTERY SHOPPING Mystery shopping is about the measurement of service quality. It is a form of participant observation which uses researchers to act as a customer or potential customer to monitor the quality of processes and procedures used in the delivery of a service. As a technique, mystery shopping is used by the UK government, and the National Audit Office records a large number of studies that use the term ‘mystery shopper’ on its website (www.nao.gov.uk) related to local authority best value. UniChem undertook its own mystery shopping study to test staff confidence in recommending products to the public to help them quit smoking. Ethical issues for mystery shopping identified by the Market Research Society are that research projects should provide management information in order to aid training and retraining plans. The client’s own staff must have been advised that their service delivery may be checked from time to time through mystery customer research. ‘The key advantage of mystery shopping is that it is able to measure the quality of services provided (according to pre-set criteria) rather than the knowledge or attitudes of providers or their self-reported behaviour.’ The practicalities of designing a robust study are difficult. Scenarios have to be designed to test exactly what you want to test. Of course one visit will not tell someone much. The questionnaire that the mystery shopper completes should be focused on objective questions, with the majority aimed at gathering factual information. The primary aim is to document precisely what happened at the point of contact, rather than how the mystery shopper feels. Mystery shopping is not just used by commercial organisations. The technique has also been used by an African American group using a PATRICIA approach (Pinellas African Americans Targeted Reduction in Infant mortality using a Community Intervention Approach). This group seeks to address issues that are multi faceted with the hope of bringing about societal change that will improve the overall quality of life for the African American members of their community. Their mystery shopping project was designed

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to be a quality assurance activity for consumers by providing a means for consumers to assess the quality of maternal and child health services they receive. In addition, the project sought to build the capacity of local consumers of these services as partners in improving and designing programmes and services.

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6 CONCLUSIVE RESEARCH – DESCRIPTIVE AND CAUSAL

CONCLUSIVE RESEARCH – DESCRIPTIVE AND CAUSAL

As you will recall from Figure 1.1 at the start of this unit, marketers have the choice between exploratory or conclusive research designs. Exploratory methods are generally used first in order to give marketers a better idea of what they should be looking at. Conclusive designs are usually used next. Within this type of research design marketers have the choice between descriptive research and causal research. Having looked at exploratory research in the last two sections we will describe conclusive designs.

6.1

DESCRIPTIVE DESIGNS

As the very word descriptive suggests, such research is used where we want to describe the market characteristics that are of interest to us. For example, we may like to know the proportion of a particular class of people who would buy from department stores offering discounts. Descriptive research is highly structured and interwoven with clearly articulated hypotheses that can be tested. The following examples are of situations where descriptive research could be relevant to the marketer’s information needs. 1 To describe the characteristics of groups such as consumers, organisations and market areas. For example, if we want to find out the profile of frequent shoppers at high street supermarkets, descriptive research is the appropriate design to use. 2 To estimate the proportion of customers who would be potential buyers of the latest brand of refrigerator introduced by a consumer goods manufacturer. We would like to find out whether this proportion would be substantial enough for the company to go for a full launch of this new product in the marketplace. 3 To find out the perceptions of customers with regard to critical dimensions of service quality that would affect the performance of a large bank in terms of profitability and growth. 4

If we would like to find out whether there is a strong association between income strata (bands) and the brand chosen in a cross tabulation, the appropriate design would be descriptive research.

Descriptive research is further split into cross-sectional and longitudinal studies. In a typical cross-sectional study we may, for example, like to test the hypothesis whether income categories and brand preference are associated. If the answer is yes, then we have to evolve an effective strategy to attract these customers into buying OU BUSINESS SCHOOL

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our brand. Unfortunately, there is one major drawback in crosssectional studies, namely we cannot identify any change in pattern over the passage of time. This deficiency is overcome when a longitudinal design is used. “How did the American public rate the performance of George Bush immediately after the Iraq war” is an example of a cross-sectional design. “How did the American public progressively change their views of George Bush’s performance during the Iraq war”, is an example of a longitudinal design. Descriptive research is undertaken using surveys which involve a structured questionnaire given to a sample target audience from whom the required information is gathered. The actual methods of gathering survey data include personal interview, telephone interview and mail interview.

6.2

CAUSAL RESEARCH (EXPERIMENTAL RESEARCH)

Causal research, also known as experimental research, is used in problem situations where the researcher is interested in establishing beyond reasonable doubt a cause-and-effect relationship between variables. In such situations descriptive research would not be good enough because it can only show that two variables are related or associated. When causal research is undertaken we have reasonable proof that one variable caused a change in another and there were no other causal factors that could have accounted for the relationship. Typically, causal research is undertaken in a relatively wellcontrolled environment where the independent variables are manipulated or changed with a view to observing the effect in the dependent variable.

An example of causal research A marketer is interested in testing the effectiveness of a sales promotion campaign in terms of raising sales. Assume that 36 retail-outlets have been chosen for this purpose. Sales (in number of cases sold) can be measured in these retail outlets prior to the promotional campaign. The campaign will be in action for 6 weeks. After the campaign is over, sales in the same retail outlets can be measured again. If the sales (number of cases sold) has substantially gone up after the campaign, then we can say that the sales promotion is effective in enhancing the sales. We must remember that experiments are costly, time consuming, and difficult to implement. They also require researchers who are knowledgeable in conducting and analysing results from formal experimental designs.

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6 CONCLUSIVE RESEARCH â&#x20AC;&#x201C; DESCRIPTIVE AND CAUSAL

Table 6.1

A comparative picture of research designs Exploratory

Descriptive

Causal (experimental)

Purpose

Generates insights, hunches, and ideas.

Describes the market characteristics and functions that are of interest.

Establishes beyond reasonable doubt the cause-andÂ effect relationship among variables.

Features

Versatile and flexible in approach. The first diagnostic step in a research design

Well articulated in the beginning with a set of specific hypotheses.

Manipulates the independent variables to identify changes/ effect on the dependent variable.

Methods Used

Secondary data, experience survey, case study method, and qualitative research

Secondary data, survey data, and observational data

Data entirely based on experiments conducted.

ACTIVITY 6.1 Which research design would you recommend in the following situations? Give your reasons: l

Determining the acceptability of a new product.

Deciding the price level for a laptop.

Studying the possibility of developing a new market in Australasia.

COMMENTARY Determining the acceptability of a new product A descriptive research design would be appropriate to use in this case. A well-articulated hypothesis as to the level of acceptability for this new product by the consumers could be specifically tested to find out whether it would be feasible to launch this new product into the marketplace. For example, the company can specify, say, at least 30% of the target audience in the test market should accept the new product, so that it is financially viable for the company to introduce it into the market.

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Deciding the price level for a laptop A descriptive research design would be appropriate to decide the right price for a laptop. Customers’ perceptions of the alternative price levels proposed by the company along with existing prices charged for similar products by key competitors in the market should be assessed. The brand image enjoyed by the company in the eyes of the customers along with the competition should also be measured. The price of the laptop should be commensurate with the quality of the product as perceived by the customers. Thus it appears that descriptive design is the best for this case. Studying the possibility of developing a new market in Australasia An exploratory research design would be suitable for this case. This could generate initial insights, ideas and hypotheses on competitor activities, the economic climate, the political climate, the behaviour of the currency (possible devaluation), trade barriers, buyer behaviour, and long-term perspective of the market. Once a set of clearly identified hypotheses emerge from the exploratory study, conclusive research could be used to confirm the hypotheses before making critical strategic decisions concerning the new market in Australasia.

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7 TECHNICAL ASPECTS OF UNDERTAKING RESEARCH

TECHNICAL ASPECTS OF UNDERTAKING RESEARCH

If you refer back to Figure 1.1 in Section 1 you will note that as well as the choice between exploratory or conclusive designs, another important set of issues surrounds the technical aspects of undertaking research. The technical aspects, as we refer to them, address areas such as sampling, data measurement, questionnaire design, data collection and data analysis. In this section we will look in a little more detail at some of the technical aspects of undertaking research. The purpose of this is to give you some insights into important issues that need to be considered when research is undertaken. Awareness of these issues is important because it can also help with an appreciation of the limitations and insights of secondary data. Knowledge of these issues can also be important when managers consider commissioning research by external agencies. The first issue that we shall look at is â&#x20AC;&#x2DC;samplingâ&#x20AC;&#x2122;. We will then look at the different types of data that can be gathered and their relative usefulness. The section ends with a description of how questions are asked in a survey. A population is defined as the set of all objects that possess some common set of characteristics with respect to a marketing research problem. Sampling refers to surveying only a sample of the whole population in order to make inferences about the population. Choosing the wrong sample will give a biased view of what really exists. The sampling design used in a research study has a major impact on the results generated by such a study.

7.1

WHY DO WE NEED SAMPLING?

The following extract highlights the importance of sampling in research:

A poll in 20 swing states published yesterday showed John Kerry still clinging to a narrow lead over President George Bush in the key election battlegrounds, but it raised questions about the reliability of such polls at a volatile point in the campaign. Some experts have pointed to differences in methodology. All three polls [this poll and two others that had markedly different findings] asked the preferences of likely voters, but each used a different questionnaire to determine who counted as a likely voter.

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More controversially, Zogby International weights its results according to party identification, adjusting them so that the proportion of its sample that identifies with each party conforms to the population as a whole. Other polling organisations adjust for height, race, and gender, but argue vehemently against weighting by party identification, saying that it is subject to change. John Zogby, the head of Zogby International, disagrees, arguing that party identification changes only glacially, and that the unusually high number of Republicans in the Gallup sample suggested it was skewed. Maureen Michaels, an independent pollster in New York, said there was merit in both approaches, and argued that the wide variation in poll results in past weeks has more to do with the volatility of the electorate than the techniques of the pollsters. â&#x20AC;&#x2DC;Poll giving Kerry lead stirs controversyâ&#x20AC;&#x2122;, Wednesday September 22, 2004, The Guardian

Key terms used in sampling Population (Universe) The population for a study is the collection of all objects that possess a specified characteristic of interest. All the participants taking the course Marketing in a Complex World at the Open University Business School is an example of a population.

Sample A sample is a subset of the population. For example, a school wants to select and send a team of 20 students from 200 students in a particular year group. The population is the total number of students (200). The sample is the 20 students selected.

Parameter The population characteristic of interest is called a parameter. For example, we might want to have some idea about the income level of a particular class of people living in Manchester. The average income of this entire class of people is a parameter we want to estimate.

Sampling frame The sampling frame is the complete up-to-date list of the units of the population of interest, from which the actual samples are selected. Examples of a sampling frame would include the mailing list bought from a commercial organisation, an association directory that contains the list of firms in an industry, or a telephone directory. Imagine a company is interested in launching a new product. Before launching the new product into the marketplace, the 68

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marketing manager of the company wants to get some ideas about the demand potential for this product. We can do this in two ways. We can ask all potential buyers in the country whether they will actually buy it, and if so how much will they buy. Alternatively, we can take a sample of the potential buyers, ask them how many units of the product will they buy, and then estimate the likely demand for the product based on the results of the sample survey. The first method is called a Census (also known as complete enumeration). It has two major disadvantages: it is time consuming and very expensive. The alternative, to use sampling, overcomes both these disadvantages. You should note though that sampling frames that are complete and up-to-date are often unavailable.

EXAMPLE 7.1 SAMPLING IBM’s practice IBM conducts an international tracking study twice a year. Translated into 14 different languages, this major research study is undertaken in 27 different countries in Europe, North and South America and Asia. The primary aim of this study is to gather meaningful trend data on mainframe computing. The sample methodology envisages selecting one out of every six sites where an IBM S/390 Enterprise Server is in operation. The target sample participants represent people in charge of IBM acquisition decisions in their enterprises. Source: Adapted from Joseph Rydholm, ‘A Global Enterprise’, Quirk’s Marketing Research Review (November 1997)

7.2

TYPES OF SAMPLING

The next diagram shows the major types of sampling strategies that researchers can use.

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Figure 7.1

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Types of sampling

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Probability sampling Probability sampling requires specification of the target population. The method for selecting the sample needs to be developed. The sample size also needs to be determined and the problem of non-response needs to be addressed.

Simple random sampling In this approach every unit in the population has the same probability of being selected. This is similar to the numbers being chosen in a lottery draw. A bank wants to conduct a study on the customers’ perception of its service quality with respect to the savings bank account holders. First, we have to prepare the sampling frame for this study. We can go through the bank’s records and get a complete list of savings bank account holders. This is our sampling frame. Assume that our sampling frame contains 400 account holders and we have to select 40 out of this 400 and interview them. How should we draw a sample of 40 account holders out of the 400 account holders? One method is to prepare 400 small paper slips, each giving the account holder’s name and account number. We can put these slips in a container, shuffle the container thoroughly and then select 40 slips one after the other from the container. A better way is to generate random numbers, for example using the Microsoft Excel spreadsheet which has a specific function for doing this. These random numbers can then be allocated to the different account holders and the people with, say, the forty highest random numbers being selected.

Stratified random sampling The example below, together with the discussion that follows, will help to explain this method of sampling.

Example of stratified random sampling Suppose we wish to estimate the distribution of expenditure by consumers across broad categories. Suppose further that we believe the expenditure pattern varies according to age, income and owner-renter status. In order to ensure that the sample includes persons from each of these categories a stratified sampling procedure could be employed. In this particular case we might stipulate three categories on age, two categories on income and

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two categories on owner-renter status. This would yield 12 different sub-groups or strata as outlined below:

Income Less than ÂŁ 30,000

ÂŁ 30,000 and over

Age

Renter

Owner

Renter

Owner

Under 40

(1)

(2)

(3)

(4)

40-60

(5)

(6)

(7)

(8)

Over 60

(9)

(10)

(11)

(12)

A simple random sample could now be selected from each of the above 12 strata thus giving representation to each of the sub-groups. Stratified random sampling is effective when there are differences within the population (it is heterogeneous) and we want to get a realistic picture of the parameters of the overall population. If simple random sampling is used in a population made up of distinct groups of people it will be highly misleading and prone to error. To put it another way, we should use stratified sampling instead of simple random sampling in the case of a heterogeneous population, because we will be able to represent not only the overall population, but also the key strata of the population.

Systematic random sampling In systematic random sampling, the units are drawn from the population at regular intervals. It is one of the easiest procedures to follow. The interval used in systematic random sampling is obtained by dividing the population size by the sample size (and then rounding this to a whole number). Let us take an example to explain the systematic random sampling procedure:

Example Suppose in a market research study we have to select 6 households out of 40 households in a street say. The sampling interval is 40/6 = 7 (after rounding). Now, select a random number in the interval between 1 and 7. For example, the number chosen might be 3. Starting with the 3rd, we then select every 7th sample. That is, the selected units are 3, 10, 17, 24, 31, and 38. In the systematic random sampling method, it is important that the units in the population be randomly arranged on the basis of the characteristics of interest we would like to measure. Why should we use systematic random sampling? There are three reasons. Firstly, it is

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very easy to implement. We have to select only the first number at random. The rest of the units are then determined automatically (using the calculated regular interval). Secondly, systematic sampling is more pragmatic in its approach than simple random sampling. Thirdly, when we have to strike a balance between precision and cost in certain situations, systematic sampling is the only way out.

Cluster sampling Consider for example one of the major problems faced with probability sampling is that we have to apply sampling procedures to a universe that is scattered across a number of wide geographic regions. In these situations, we will have to cover a great deal of distance before we have access to the units in the sample. If we decided to do a simple random sample survey of the UKâ&#x20AC;&#x2122;s highest income residents, the interviewers employed by us would have to do a huge amount of travelling around the country. It is for this reason the cluster-sampling method is adopted. The steps involved in cluster sampling are: l

We first divide the population into a number of clusters using geographic boundaries

We then select a random sample of clusters (for example, selecting cities) from this population of clusters (cities)

Next, we can either measure all the units within the randomly chosen clusters or do further random sampling in each cluster.

Strictly speaking, cluster sampling only applies when we measure all the units in the selected clusters. If we do further sampling within each cluster by adopting for instance, a simple random sampling or a stratified random sampling method, it becomes a case of multi-stage sampling. Note the similarities and differences between cluster sampling and stratified sampling. Both involve the division of the population into mutually exclusive and exhaustive subgroups, although the criteria used are different. The criteria for dividing the population are not the key in differentiating the techniques. The key ingredient that distinguishes the procedures is that with stratified sampling, a sample of elements is selected from each subgroup. With cluster sampling, one chooses a sample of subgroups (Churchill, 1999).

ACTIVITY 7.1 In which of the following sampling procedures are the units in the population divided into separate groups, each with a common characteristic? (a) Systematic sampling (b) Cluster sampling (c) Stratified random sampling.

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COMMENTARY The right choice is (c). In stratified sampling, we divide the population into a number of groups called strata in such a manner that the units within a stratum are similar (common characteristics). Homogeneity within a stratum and heterogeneity among strata are the distinguishing features of stratified sampling. The other choices are incorrect.

The box below shows how a study about a specific market sector (produced for commercial organisations) details the sampling methodology it used. This information is produced in the marketing literature for the study and gives potential buyers of the study some indication of its scope and the possible quality of the findings.

THE CAR BODY REPAIR MARKET IN NORTH WEST EUROPE This report on the car body repair market in north west Europe summarises the findings from a research study on the car body repair markets in France, Germany, Italy, the Netherlands, Portugal and Spain. This report is aimed at providing a detailed analysis of the market for operators of bodyshops, insurance companies, motor dealers and manufacturers, and organisations supplying body repair outlets with products and services. The research methodology included a quantitative study carried out among a sample of 100 bodyshops throughout the UK. Yellow pages for each region in the UK formed the basis of a sampling frame for bodyshops, as this would include all bodyshops in operation. From an alphabetical list of bodyshops in each region, a quota was decided upon to form the sample for interviews, with the size of the quota in each region proportionate to the regionâ&#x20AC;&#x2122;s size in relation to the rest of the country. From the alphabetical list, a bodyshop was selected at equal intervals to avoid any bias that could arise from an alphabetical listing. Interviews were then carried out selecting bodyshops at equal intervals from the list until the required number of interviews to fulfil the quota had been completed. http://www.just-auto.com/store/products_detail.asp?art=20007&lk=nd02

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7.3

NON-PROBABILITY SAMPLING

Non-probability sampling, as the name implies, involves selection of units from the relevant population without using any probability principle. This is the fundamental difference between non-probability sampling and probability sampling. Even though the non-probability sampling has advantages such as reduced cost, speed, and ease of implementation, the bias, or the reliability associated with it cannot be appraised objectively. Another deficiency of non-probability sampling is that it is simply not possible to generalise results from the sample to the population. In spite of these shortcomings, non-probability sampling has a place in marketing research. Its importance in pre-testing instruments and exploratory research is firmly established. Probability sampling for these tasks is not worth the extra cost.

Convenience sampling Convenience sampling is a method of sampling in which the researcherâ&#x20AC;&#x2122;s convenience forms the basis for the selection of units. Academics use college and university students in studies involving attitudes towards co-education essentially as a matter of convenience. In consumer panel studies, organisations use clients who are easily available to them as respondents for giving their views and impressions on products and services. In many marketing research projects, we simply look for volunteers to participate.

Expert opinion sampling Expert opinion sampling involves assembling a set of people who have knowledge and expertise in certain key areas that are crucial to a number of marketing research problems. In demand forecasting for a new product, we use the expert opinion method to arrive at a reasonable forecast. The chief advantage of this sampling is that it acts as useful support material for some of our decision situations where virtually no other data are available. The major disadvantage is that even the experts will bring along with them their prejudices, likes, and dislikes that might totally distort the results.

Quota sampling In simple terms, quota sampling is stratified random sampling without the probability principle being applied to the selection of the sample units. Let us assume in an opinion study we want both men and women to participate. Let us also assume that in the

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population category of interest, 70% are men and 30 % are women. If our sample size is fixed at 300, we will have a quota of 210 men and 90 women. It doesnâ&#x20AC;&#x2122;t matter how we get them as long as we get them and meet the quota. There are some socioeconomic studies where quota sampling is the only method to be used in view of practical considerations. Drawing any possible conclusions from quota sampling will be highly tentative. None of the statistical inference techniques can be applied when we have followed any non-probability sampling procedure, although in reality they are.

ACTIVITY 7.2 A company is interested in launching a new product. This product, if introduced, is the first of its kind in the marketplace. The problem facing the company is a reasonable assessment of the demand potential for this item. This is really a difficult task in view of the fact that no past data are available. Discuss how you would proceed with this task. Make sure you highlight the strengths and limitations of the method you propose.

COMMENTARY We can go for the Expert Opinion sample survey in this case. A group of experts from diverse background such as marketing, sales, finance, operations, and purchasing can be asked to make forecasts for the new product. A consensus can then be arrived at on a forecast figure. The advantages of this method: (1) It is fast and efficient. (2) It is timely and based on good information content in terms of industry data, competitive strategies, and reactions from customers and distributors. (3) It uses the collective knowledge of the experts. The disadvantages of this method: (1) Experts also can make mistakes. (2) Prejudices of experts can invalidate the forecast. (3) The group dynamics of the experts could be affected by the dominating attitude of one person. He who shouts the loudest gets his way.

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We have previously looked at the research involved when

Microsoft entered the Korean market with its Xbox gaming console.

Here we look at the sampling undertaken by the firm for both its

qualitative and quantitative research.

For the qualitative study four focus groups (three male and one

female) of 14 to 26 year olds were assembled. The criteria they

were selected on were as follows:

– Mix of hard core only and hard core/serious gamers – Mix of PC and console gamers – Each group was made up of 8-10 people The quantitative survey was performed using 350 telephone interviews and the people had to meet the following requirements: – Minimum of gameplay of one hour per week – Primary gamer in the household – Aged between 10 and 30 years. The sample was approximately 83% male and 17% female and contained 150 hard core (15+ hours per week) players, 100 serious players (4–14) hours per week and 100 casual players (0-3 hours per week). Although game companies have defined the gaming audience as 100% male, the reason why Microsoft wanted to have some female representation was because of the importance of that segment to Korea, and also because they wanted to have the data in order to defend an all-male marketing strategy should they need to pursue it. Microsoft used clustering in order to identify three mutually exclusive segments that were based on information about the gaming behaviour, attitudinal measures, purchase influence and gaming preferences.

7.4

DATA MEASUREMENT

The way in which data is measured is an essential aspect of questionnaire design. The questionnaire will involve a combination of all the measurement scales and not just one type of measurement. The way in which data is gathered ranges from nominal data, ordinal/rank data, to interval and ratio scaled data. Nominal data is used to label an item or characteristic of interest to the market researcher. B825 is an example of a code used for labelling. Nominal data cannot be manipulated numerically in a fashion, but we can use them for counting in order to work out the frequencies and percentages. With ordinal (or rank) data we use numbers to rank objects or attributes. For example, consider the customer preference for our

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brand. On a scale that ranges from 1 to 5 the lowest preference is rated at 1, an average preference is rated at 3, and a strong preference is rated at 5. Simple arithmetic operations are not possible for ordinal data and the ‘distance’ between ranks (or objects) cannot be measured. An example of ranking is given below.

The German chemicals giant Henkel, in analysing its Pritt adhesive brand, had 100 respondents and found an average rating of 6.8 (on a 1–10 strongly disagree to strongly agree scale) for the statement ‘Pritt conveys confidence’. The company gathered similar data for its main competitor UHU. This information was subsequently used in a snake plot. The plot showed that UHU is seen by consumers as being a stronger general purpose brand, whereas Pritt is seen as being useful for special light-duty purposes, such as sticking paper for children. Analysing consumer perceptions, Dolan, R.J. Dec 12, 2001, Harvard Business School case No 9-599-110

Interval Data: for example, frozen-food distributors are concerned with temperature, which is an interval measurement. Interval data have an arbitrary zero point. With such data we can measure the ‘distance’ between objects. Ratio-scaled data: this is the highest level of measurement that has the requisite desirable properties and allows us to perform all basic arithmetic operations, including division and multiplication. Data measured on a ratio scale have a fixed zero point. Examples include business data such as cost, revenue, market share, and profit. Clearly there are advantages when it comes to data analysis in having ratio-scaled data. The actual choice of questions in a survey will, however, also need to take into account issues such as whether people will be able to remember specific numbers (such as income) or indeed whether they will be willing to disclose them. Furthermore, the research objectives for a project may not necessarily require ratio-scaled data and ordinal (or rank) data may be sufficient.

7.5

SURVEY QUESTIONS

Questionnaire design is the main vehicle of primary data through which the relevant information is collected from the target audience. A questionnaire will consist of structured/unstructured questions, questions that are disguised, and questions that will involve rating scales. The nature of the questions to be included depends on the nature of the research problem.

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The following example shows the links between the overall goals of a study and the questions that were asked in the questionnaire used to collect the data.

ACCESSIBLE TECHNOLOGY SURVEY FOR MICROSOFT In 2003, Microsoft Corporation commissioned Forrester Research Inc., to conduct a comprehensive, two-part study (Phase I and Phase II) to measure the current and potential market of accessible technology in the United States and understand how accessible technology is being used today. The goals of the Phase I study were to identify the range of physical and cognitive abilities among working-age adults and current computer users in the United States, the types of difficulties and impairments that limit the scope of activities and their degree of severity, and the number of people who could potentially benefit from using accessible technology. This information, coupled with aging population trends, can help to explain the aging population's impact on computer use and need for accessible technology.

Survey questions The survey was designed to identify individuals who self-identify as having a difficulty or impairment, as well as those who do not consider themselves to have an impairment but do report difficulty with tasks.

Task-based questions These are designed to understand difficulty with ordinary daily tasks in each of the difficulty/impairment types. The types of questions asked include: "Please indicate how often, if ever, you have difficulty seeing the words and letters in ordinary newspaper print because of your eyesight. If you usually wear glasses or contacts, please indicate whether you have difficulty while wearing glasses or contacts," and "Please indicate how often, if ever, you have difficulty using a keypad on a phone/dialling the phone because of physical difficulties with your arms, hands, wrists or fingers." http://www.microsoft.com/enable/research/appendix.aspx

The distinguishing feature of a well-designed questionnaire should be its ability to convey the proper message and its ability to convey that message clearly. Figure 7.2 describes the steps involved in the design process of a questionnaire. Each step is important and the sequence of the steps will have to be adhered to while constructing a questionnaire.

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��

Figure 7.2

The steps involved in the design process of a questionnaire

Rather than consider each of the above stages in turn, we consider some broad issues in questionnaire design.

Structured vs. unstructured questions A specific question is structured when its wording is to be employed without deviation from one respondent to another. The level of structure is increased when respondents are given predefined responses from which they can choose. An unstructured approach would be used when the researcher may not be sure which structure is most appropriate. In exploratory research the problem has not been fully defined. Hence, an unstructured approach is required in order to obtain a precise definition. In other situations, complex relationships between the issues of interest to the researcher may exist – compelling the researcher to probe the respondents and be flexible in his/her approach.

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An example of a structured versus unstructured approach A study of supermarket image asked a set of consumers to indicate their opinion regarding prices charged by different kinds of stores. Short descriptions were presented, and the respondents were asked, “Give the answer that comes to your mind first – very quickly”. This is a highly structured approach; deviations from the precise wording or the introduction of additional phrases might change the results significantly. “A store that offers lots of extra services” may bring about an entirely different answer compared to “A store that offers lots of extra services – like the Carrefour up the street”. The unstructured approach here will have an advantage because it doesn’t inhibit the spontaneity of the respondents in providing answers. We can ask, for example, “What are your views regarding the prices charged by different kinds of stores?” This is an openended question and provides an opportunity for the researcher to really understand what is going on in the minds of the consumers.

Methods of administering the questionnaire This is actually part of the fieldwork in terms of administering the questionnaire to the target respondents and then eliciting the required information. There are three widely used methods: (1) Personal Interview (2) Telephone Interview (3) Mail Survey. Table 7.1 lists the key issues. Nowadays there are also online surveys. There are, of course, significant advantages for online research with customers. This approach can be cheaper and quicker than traditional alternatives. It can also be less intrusive and more convenient for customers. The anonymity of the ‘net’ can encourage more frank answers to open-ended questions. It is also argued that it is easier to manage such answers – should the organisation wish to take action based on them, since they can be forwarded in an email to the relevant manager.

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Table 7.1

The key issues of different types of interview Personal interview

Telephone interview

Mail survey

Basic Characteristic

Face-to-face communication.

Direct communication without physical presence.

Selfadministration without physical contact.

Advantages

Extremely flexible, large and complex questionnaires can be administered effectively. Response rate is high

Quick, inexpensive, and has a fantastic national coverage capability. Response rate is moderate.

Low response bias (anonymity), good geographic reach, respondent convenience, and low cost.

Disadvantages

Interviewer and Interviewee bias, certain areas are inaccessible, slow in execution and expensive

Short questionnaire, difficult to reach the target audience, cannot use visuals, complex instructions and response formats not possible.

Highly standardised, inflexible, timeconsuming, and low response rate.

However, one of the reasons why people have been slow to take to online surveys is that switching to a new survey method means different response patterns, so new data are not strictly comparable to what had been elicited from previous surveys using older methods, such as postal surveys. Although using the net for general surveys may have problems, there are nevertheless advantages when it comes to doing speciality research. This is because researchers can send out lots of qualifying questionnaires (via email) and quickly find the relatively small group of people that they are interested in. The same process by ordinary mail or telephone would take weeks. For example, Eli Lilly used such an approach for finding people with obscure ailments and an organisation wanted to survey only people who used a virtual bank. In both of these instances using the net was much more effective than older approaches could ever have been. Emails sent to large numbers of possible respondents quickly found, in both of these examples, the niche groups of people the researchers were looking for. However there are obvious problems with using the internet as a means of undertaking research. The following extract is from an article by MORI (a commercial opinion research agency).

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MORI, along with other market research agencies, attempt to interview representative samples of the general public. In order to do this, we need to take the whole of the adult population – men and women, rich and poor, young and old, north and south – as our sampling "universe". Internet polling companies, by definition, start by excluding all those who do not have access to the web – still more than half the UK population. Furthermore, respondents are self-selecting, consisting of those who volunteered to join in, usually after visiting certain websites. There is a real danger, therefore, of results being biased towards those with more interest in political issues than average. (Incentive payments may alleviate this problem but can't solve it – especially if panel members have to take the time to complete tens of surveys to get any payment.) Internet polling is usually based on members of a panel who are repeatedly interviewed. But, as researchers have known for decades, panels can quickly become unrepresentative because the interviewing process itself conditions the minds of the respondents in a way that the rest of the public have not been subjected to. We are also cautious about the checks that are possible to verify the samples. The internet is notorious for people lying about their age and sex. Net pollsters may think that they are interviewing a 65 year old woman from Birmingham, but who is she – or he – really?

Determining the content of individual questions The content of each question must be linked to the research objectives. Each question should also meet the ‘UAW’ criterion: U stands for “will the respondent Understand the question”? A stands for “will the respondents be able to provide the Answer to the question”? W stands for “will the respondent be Willing to provide the answer”? A survey involving school pupils had a question on ‘date of birth’. In their pre-testing of the questionnaire, researchers found that many pupils could not understand what this meant. The question was replaced by, two separate questions, ‘when is your birthday’ and ‘how old are you’. This information, in conjunction with the time of year the survey was undertaken, allowed researchers to work out the pupils’ date of birth.

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ACTIVITY 7.3 Critically examine the following question and modify them as appropriate. What portion of your 2003 income was not earned?

COMMENTARY According to the UAW criterion, most respondents will not ‘Understand’ the precise meaning of earned income. This question also generates antagonism and bias even among those respondents who could understand the meaning of earned income. Therefore, to this question in its present form, by and large the respondents may not be ‘Able’ to provide the answer and may not be ‘Willing’ to provide the answer. The question could be reworded as: “Out of the total income received in 2003, how much was due to your profession, and how much was due to other sources”? In other sources, we can provide ‘stock’, ‘dividend income’, ‘income from rent’ etc. and ask the respondents to tick the appropriate choice. The specific wording of a question can ‘lead’ the respondent to give expected/favourable responses. There are a number of ways of doing this, but they all in effect mean, “This is the correct answer, isn’t it?” An example of a leading question is, “Did you watch the entire program, including all commercials in their totality”?

7.6

DATA COLLECTION

The principal purpose of data collection is to gather worthwhile data that would provide answers to the research problem. Problem definition deals with “What do I want to know about?” while the data collection addresses the issue “How can I obtain the information required?” No revision to the problem definition should be made because of the data collection considerations. This would amount to getting the right answer to a question that is inappropriate for the marketer. Changes, and revisions in the problem definition are to be expected, but they should not be allowed to undermine the basic reason for doing a research project. Modifications in data collection procedures because of problem definition needs are allowed. In other words, we must do a thorough job in the planning stage in terms of a well thought out problem definition. Data collection methods must meticulously follow the research problem and the associated objectives so that the end results of the study are of a very high quality. It is in this context that the role of data collection becomes paramount. ��

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DATA ANALYSIS

Data analysis plays an important role in marketing research in terms of drawing meaningful inferences from the research project. A set of actionable recommendations flows from the analysis of data. Basically, there are two types of analysis: the first type is based on qualitative research data and the second type is based on quantitative research data. In this section, we will look at the analysis of quantitative data.

DESCRIPTIVE STATISTICS

8.1

The survey data collected is known by the term ‘raw data’. Raw data will have to be processed into ‘information’. Only information is useful for decision-making or preparing a set of actionable recommendations. Descriptive statistics is a fundamental branch of statistics that converts data into information.

��

Figure 8.1

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The processing of raw data into ‘information’

The meaning of statistics By statistics, we mean methods specially chosen for the collection, classification, analysis, and interpretation of data. In most research studies, the bulk of the analysis involves descriptive statistics and it is further substantiated by advanced statistical techniques wherever they are necessary. Descriptive statistics is concerned with: l

data summarisation

charts

tables.

It processes raw data into information. Data summarisation includes the following: l

frequency distribution

histogram

cross-tabulation

measures of central tendency

measures of dispersion.

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A frequency distribution is a summary table in which raw data are arranged into classes and associated frequencies. Classes represent categories, or interval groups, which contain a lower limit and an upper limit. Classes are formed conveniently following certain guidelines. Against each class, you count and place the number of observations that fall into it. When you do it for all classes, it becomes a frequency distribution.

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A histogram is a graphical depiction of the frequency distribution in which the x-axis represents the classes and the y-axis represents the frequencies (as illustrated in Figure 8.2 below). Rectangular bars are erected at the boundaries of each class with heights proportional to the frequency of that class. A histogram depicts the pattern of the distribution emerging from the characteristic being measured.

Figure 8.2

A sample histogram (frequency distribution)

ACTIVITY 8.1 What do you foresee as uses for histograms in marketing research?

COMMENTARY A histogram will show the pattern of the characteristics that can be measured in a research project. For example, it can depict the consumption pattern for a product. A histogram can be a valuable tool when it comes to comparing different markets, different regions, and different competitors with regard to sales performance of important products. The marketing manager can easily discern the pattern using the histogram. He or she can get to know whether the pattern shows symmetry or skewness with regard to the market characteristic of interest. A histogram can also be used to evaluate the performance of different salespersons selling the

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same product in the same region. At a glance, it will show who has achieved the maximum sales quantity and the relative spread of the distribution around the average sales quantity.

Cross-tabulation A cross-tabulation (also called contingency table analysis) is a table containing frequency data corresponding to two categorical variables, one for the row and one for the column. Cross-tabulations enable the investigator to find out whether there is relationship between two categorical variables. For the purpose of interpretation, frequencies in cross-tabulations could be converted into percentages.

Example of a cross-tabulation A market research firm wants to answer the question: “Are customers from small companies more likely to be profitable in terms of marketing services (for example, consulting and training) than those from larger companies?” A cross-tabulation carried out reveals that the majority of small companies (fewer than 300 employees) produce high service profits, while the majority of large companies (more than 2000 employees) produce low service profits. Cross-tabulation is very frequently used in marketing research projects. It is difficult, if not impossible, to identify one research project without any cross-tabulation. It answers important hypotheses such as “are different income categories associated with the selection of brands?”, “are educational levels associated with the selection of jobs”? The biggest advantage of cross-tabulations is that it can be easily understood by non-statisticians in terms of the insights generated. The following example cross-tabulates income with the intention to enrol in private education.

Pattern of Internet Usage <$7,000

$7000 15,999

$16,000 and above

Total

Most interested

20.4% (38)

11.6% (46)

7.6% (37)

11.3% (121)

Moderately interested

19.4% (36)

11.9% (47)

17.9% (87)

16% (170)

Uncertain or uninterested

63.7% (112)

36.3% (302)

74.5% (362)

72.5% (776)

Total

100% (186)

100% (395)

100% (486)

100% (1067)

Gender

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The figures in the table show that the people with higher income are less likely to be interested than the people with middle income and the lower income group is most interested. The probability of a household enrolling is 0.11 (i.e. 121/1067). However, the probability of a household enrolling if that household’s income is less than $7,000 is 0.2 (i.e. 38/186). Statistical analysis can be undertaken to evaluate the association between income groups and degree of intentions.

Measures of central tendency Summary measures of ‘central tendency’ are used in all survey research. Examples include average consumption of refined oil in a household, average market share expected in the first year for a new product, and average price a consumer is willing to pay. The widely used measures of central tendency are arithmetic mean, median, and mode.

What is central tendency? Whenever you measure things of the same type, a fairly large number of such measurements will tend to cluster around the middle value. The question that arises is: “Is it possible to identify one typical average in such a manner that the remaining items in the data set will cluster around this value”? This value is called a measure of ‘central tendency’. Other terms used are ‘measures of location’ and ‘statistical averages’. Arithmetic mean (also called mean) is the most common measure of central tendency used by market research managers in survey research. Since survey data are based on sampling, the mean is a point estimate for the characteristic to be studied in a project. It is calculated as the sum of all observations in a data set divided by the total number of observations. For example, consider a data set containing the values 4, 3, 6, 5, 3, 3. The arithmetic mean = (4+3+6+5+3+3)/6 =4. Caution: arithmetic mean is affected by extreme values or fluctuations in survey data. It is not the best average to use in research project when the data set contains extreme values (i.e. very high or very low values). The median is the middle observation when data are arranged in ascending or descending order. In other words, the data are ranked and then the middle value is picked up. The median is such that 50% of the observations are above the median and 50% of the observations are below the median. The median is a very useful measure in the context of consumer preferences and rating. It is not affected by extreme values but is

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affected by the number of observations. It may be mentioned here that a large number of market research studies that deal with ranked (or ordinal) data use the median extensively as a summary measure. The Mode is that value which occurs most often. In other words, it has the maximum frequency of occurrence. The mode is not affected by extreme values. For example, the mode is a very useful measure when you want to keep the most popular shirt in terms of collar size in the inventory, say during festival season. Another example where mode is appropriate to use is in determining the most typical shoe size to be kept in stock in a shop selling shoes. Median and mean will not be helpful in these situations. Caution: In a few problems in real life, there will be more than one mode such as bimodal and multi-modal values. In these cases the mode cannot be uniquely determined.

ACTIVITY 8.2 In a market research study, it is found that the life of 10 flashlight batteries (in number of hours) are as follows: Find all the three measures of central tendency. 340 350 340 340 320 340 330 330 340 350

COMMENTARY Arithmetic mean = (340+350+340+340+320+340+330+330+340 +350)/10 = 338 Mode = 340 (because it occurs 4 times which is the most frequent occurrence) Arranging the data in ascending order it becomes: 320 330 330 340 340 340 340 340 350 350 The median = (340 + 340)/2 = 340 i.e. the average of the 5th and 6th measurement in the ordered set.

Measures of dispersion (spread) Measures of dispersion point out how large the spread of the distribution is around the measure of central tendency. It answers unequivocally the question, â&#x20AC;&#x153;what is the extent of departure from the average value for different groups having identical averages?â&#x20AC;? It is important to study the measure of central tendency along with dispersion so that the spread around the mean value can be gauged.

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ACTIVITY 8.3 In a market research study, the performance of three salespersons in terms of number of units sold for the same product on four quarters give the following figures. Look at the data and give your comments. Quarter

Salesperson 1

Salesperson 2

Salesperson 3

25000

24000

18000

25000

26000

32000

25000

23000

15000

25000

27000

35000

COMMENTARY If you carefully study the above table, you will notice that the average sales achieved (per quarter) in units is 25000 for all the three salespersons. Salesperson 1 has no variation at all and in all quarters has sold identical numbers of units. Figures of salesperson 2 show some variation but it is not too large. Hence, the average is still meaningful as there are no extremes. Salesperson 3 has a high level of dispersion or scattering around the average of 25,000, from 15,000 to 35,000. Hence the average by itself in this case could be misleading. The moral of the story is that statistical measures of central tendency and measures of dispersion have to be studied together to draw meaningful conclusions. Range is the simplest of all measures of dispersion. It is the difference between the maximum value and the minimum vale in the data set. The most popular measure of dispersion is the standard deviation. It forms the basis for inferential statistics. It is a classic measure of dispersion. It has many advantages over the other measures of variations or dispersion. It is based on all observations. It is capable of being algebraically treated, which means that you can meaningfully combine the standard deviations of many groups. The relative spread is measured by a term called the coefficient of variation (CV). It is the ratio of the standard deviation to the mean of a distribution. The greater the CV, the greater is the percentage spread. As a manager, you would like to have a small CV, so that your assessment of a situation is robust. The risk arising from making decisions based on an average is minimised.

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ACTIVITY 8.4 There are two salespersons working in the same territory. The sales performance of these two (measured over a number of years) with regard to selling PCs is given below. Comment on the results. Salesperson 1

Salesperson 2

mean sales (one year average): 300 units standard deviation: 12 units

mean sales (one year average): 400 units standard deviation: 100 units

COMMENTARY The CV is 12/300 = 0.04 or 4% for Salesperson1 and 100/400 = 0.25 or 25% for Salesperson 2. Salesperson 1 has much smaller variations in performance than Salesperson 2 - both as an absolute value (the standard deviation) and with less relative dispersion (i.e. the CV). Salesperson 2 has a very high standard deviation from his average sales. However, Salesperson 2 achieves a higher average, but his performance is not consistent.

8.2

INFERENTIAL STATISTICS

Inferential statistics are used to understand population characteristics (also called parameters) from a sample. It involves point estimation, interval estimation, and hypothesis testing.

Example of inferential statistics Assume that you as a marketing manager would like to identify a niche market for your new product. You know from your experience that an accurate assessment of the income of a typical family is crucial. The average income of this typical family in the population is estimated to be £36,000 based on data from a sample survey. In this example, an average income (£36,000) based on sample is a point estimate of the whole population. The average income that falls within a statistically-formed interval of, say, £36,000 plus or minus £4,000 (one example of an interval) is called an interval estimate. The statement that “the average income in the population is more than, say, £33,000 per year” is an example of an hypothesis. Caution: Inferential statistics makes the crucial assumption that the sampling method is random (i.e. based on probability sampling)! Inferential statistics are built on the foundation of a normal probability distribution as illustrated in Figure 8.3 with the probability being the vertical axis and the variable along the x-axis. OU BUSINESS SCHOOL

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Figure 8.3

Picture of a normal distribution

The concepts of interval estimation and hypothesis testing need the crucial assumption of there being a normal distribution of the variable (in this case of family incomes as the x-axis). Its outstanding properties make the role of normal distribution paramount for researchers in general and marketing researchers in particular. The mode is the most-frequent or probable measurement - at the peak of the curve. A normal distribution is symmetrical about the peak (bell-shaped). In this case, there are as many measurements above the mode (a given distance from it) as there are below it. Given this symmetry, the average (or mean) of the distribution is also at the centre of the distribution (see Figure 8.3). Many variables in nature (e.g. height of male citizens) follow or approximate to a normal distribution.

Properties of the normal distribution 1 The normal distribution is a continuous distribution looking like a bell. Statisticians use the expression ‘bell-shaped distribution’. The word continuous means any value (including decimals or fractions) are permitted. For example, the market share of a product could be 15.73%. It need not be a whole number. 2 For a normal distribution, all the three measures of central tendency namely mean, median, and mode are all equal to one another. 3 The normal distribution is symmetrical about its mean value. This implies, if we take equal deviations on either side of the mean, the areas covered on either side of the mean are equal. This property is used in the context of constructing confidence intervals (interval estimation).

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8.3

AN OVERVIEW OF DATA ANALYSIS TOOLS

There are 4 types of analysis discussed below. l

factor analysis

cluster analysis

discriminant analysis

conjoint analysis.

Factor analysis ‘Social class’ may be an important basis adopted by retail chains for market segmentation. What is social class? How is it defined? Can we combine occupation, source of income, house type, and dwelling area into a single number using some heuristic method and call it social class? Any weighting scheme that we use in this regard will be highly subjective. Factor analysis provides an approach that reduces a set of variables into one or more underlying dimensions (factors). Factor analysis collapses those variables that are correlated with each other into a few manageable numbers of dimensions by extracting as much of the original information content as possible. Factor analysis can also be used to identify underlying dimensions that make up the image of a brand or store. Likewise, one or more composite performance measures for salespeople might be generated using the same technique.

NESTLE REFRIGERATED FOODS Nestle used qualitative research to identify twenty seven attribute statements used by consumers to describe the differences between brands of pasta. These statements were for example, ‘appetising appearance’, ‘does not stick’ and ‘don’t have time to cook’. In the quantitative phase of the research, respondents were asked to name the brands of pasta they usually purchased, and then indicate the degree to which the 27 attribute statements described each of the brands in their usage set. A multivariate research technique called factor analysis was used to reduce the 27 attributes to the major criteria that customers used to differentiate different brands of pasta. Although five major criteria emerged which represented the consumer’s macro view of the pasta category, three explained nearly all of the variance in the brand ratings data as collected on the 27 attribute statements. These three major criteria were, ‘ordinary main meal’, ‘quality’ and ‘a light meal side dish’. The next step was to plot the positioning of pasta competitors. Based on this analysis the ‘high quality’ positioning as a ‘light/meal side dish’ was the clear winner. Bell and Rangan (1997)

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Cluster analysis Cluster analysis, like factor analysis, also attempts to find systematic variations when the decision maker is overwhelmed by plethora of data that seem to have no central message. Consider a research project that has collected information concerning 25 retail chains on 40 different characteristics. Factor analysis would attempt to condense the 40 variables into a few dimensions, such as service orientation, quality level, and width of assortment. Clustering would attempt to form two or three groups of chains in such a manner that the chains within each group are as homogeneous as possible on the characteristics. Cluster analysis is used for market segmentation. While factor analysis collapses variables into underlying factors, cluster analysis collapses objects into meaningful clusters.

Ford’s advertising agency felt that the market for Ka in France could be segmented on the basis of consumer lifestyles and behaviour. They had identified four attitudinal segments: 1 ‘freedom lovers’, described as being outgoing, social and active, 2 ‘attention seekers’ innovators, opinion leaders and flashy 3 sensible classics (responsible risk-averse traditionalists) 4 no nonsense neutrals, brand wary, TV watchers, unenthusiastic consumers. The agency had then shown the level of interest each attitude segment had towards Ka. It became clear that ‘freedom lovers’ had the most Ka choosers, whereas the no-nonsense neutrals had the most Ka non-choosers. The agency felt that the first two groups would be the targets for Ka. ‘Ford Ka, the market research problem’, Cothier, G. et al 2003.

ACTIVITY 8.5 What concerns do you think the company could have on targeting these two groups – notwithstanding the marketing research information?

COMMENTARY The company felt that advertising budgets may have to rise in order to attract what could be an elusive attitudinal target. The company preferred demographic based segmentation that could be more easily translated into an advertising campaign.

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Discriminant analysis With pre-determined groups, discriminant analysis attempts to find how different these groups are. The basic purpose of using discriminant analysis is to classify objects into one or more groups on the basis of a well-defined set of measurements. For example, when a bank gives loans to customers it always faces the risk of not being able to collect the principal amount from the customer. Discriminant analysis can evaluate and place a customer into groups such as a high-risk group, medium-risk group and low-risk group. A high-risk group indicates that the loan is unlikely to be recovered; a medium-risk group indicates that the loan is partially recoverable with tremendous efforts from the service providers; a low-risk indicates that the chances of recovery are very high for the bank. The primary objective of the bank is to predict whether a customer will fall into any one of the risk groups based on certain measurements of important variables. This will help the bank to decide whether to give a loan or not to a particular customer.

Conjoint analysis Conjoint analysis is used to determine utility values that customers implicitly assign to various levels of important attributes used in evaluating objects. By utility we mean the level of benefit that a customer derives from using a product or service. The utility values help the marketing manager ascertain the relative importance of the different features of a product or service. For example, using conjoint analysis, an airline can determine how customers perceive the utility value for each passenger service e.g. early check-in, in-flight movies and the total utility for the best combination of passenger services. Conjoint analysis is being very widely used today in application areas such as measuring brand image, insights into how consumers make choices within an existing market, new product development and simulating market share. ��

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9 CONCLUSIONS: MARKET RESEARCH

CONCLUSIONS: MARKET RESEARCH

We have seen in our coverage of marketing research (Sections 2–8) that the activity should be thought of as a process, although one where not all the stages may always be undertaken in the same sequence or indeed at all. We have seen that driving the marketing research process are the information needs of managers – who will also take into account budgetary constraints when deciding which research methods should be used. We have also drawn attention to the different ways in which research can be undertaken, and that a researcher’s choice of method will depend on the nature of the information that they want to gather, as well as time and money constraints. Different methods that can be used to gather information vary in terms of the nature of the insights that they can offer. While in-depth interviews can be used to find out what people think and why (in their own words), surveys are more suitable for measuring what large groups of people think, and the extent of the differences between them. Fundamental to all research is the issue of quality and how this is achieved will vary depending on whether qualitative or quantitative methods are being used. ��

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10 HISTORICAL BACKGROUND OF SCENARIO PLANNING

HISTORICAL BACKGROUND OF SCENARIO PLANNING

Strategic planning has gone through several phases over recent decades. The golden age came after the Second World War, when Keynesian demand management produced not just stability in the economic environment but the predictable growth which organisations crave. Under these near-ideal conditions, planning was usually a matter of projecting forward the well-understood trends which were there for all to see. Understandably, mathematical modelling, latterly using the newly available computing power, became the favoured solution. Then, in the 1970s, all the certainties fell apart. It may have been the result of the oil price-rise shocks of the early 1970s or Richard Nixon’s dismantling of the Bretton Woods agreements – by which the dollar was used to stabilise the global financial markets. Whatever the cause, the prevailing climate suddenly displayed a most disagreeable level of uncertainty. The planner’s first response was to develop even more sophisticated mathematical approaches. As the 1970s progressed, even the new scenario-based techniques were used as vehicles for computerised approaches, such as cross-impact matrices, and large planning staffs in corporate headquarters were the rule. By the 1980s, however, the failure of these specialists to improve their organisation’s performance led to the dismantling of their operations, and to such planning eventually falling into disrepute. The subsequent search for new certainties, for easy simplicities that managers could understand, brought us nearer to home; first through competition policy and then reverting to totally internal issues, such as cost-cutting and re-engineering and their solution of salvation by redundancy. All of these ideas held some truth, and matched the depressed mood of the recessionary times but, ultimately, none of them could resolve the problems posed by the prevailing uncertainties. Despite some likely reduction in their numbers and impact, successful forecasting will still, for a long time ahead, have to take account of these uncertainties. One response to the challenge posed by volatility and uncertainty in the external environment is to consider alternative views of the future by developing scenarios. The use of scenarios in long-term marketing planning is the main theme of this part of the unit and will be developed in some detail. This progression in approaches to planning is illustrated in Figure 10.1. First, though, we shall consider some of the limitations of conventional forecasting.

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Figure 10.1

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The evolution of scenario analysis

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11 FORECASTING

FORECASTING

There is often confusion between budgeting and forecasting, and they are sometimes used interchangeably. Indeed, many organisations use the term ‘forecast’ for sales targets and ‘budgets’ for the resources which support these. In this unit, however, we use these terms as follows. Forecasting is the prediction of what will happen in the future, usually expressed, in the short and medium term, in terms of statistics. The important element is that forecasts are based on certain general assumptions – typically that all other things are equal, without any special action being taken by the organisation making the forecast. These assumptions should be made explicit – but frequently they are not. Budgeting expresses what the organisation believes is specifically achievable, and intends will be achieved, by its planned actions. The budget is a target – typically expressed in financial terms – which the organisation sets for itself, so that the budget becomes management’s commitment to action. In theory, if not in practice, the unbiased forecast (albeit usually based on hidden assumptions) should be an input into the subsequent budget, which is then a measure of where the organisation intends to go – and indeed is targeted to go, as shown in Figure 11.1. Many so-called forecasts prepared by organisations should, therefore, more correctly be called budgets.

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Figure 11.1

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The budget process

This represents not just a dispute about terminology but a major problem of confused attitudes. The forecast is the basis for planning: it has to be as accurate and unbiased as possible. The budget is directly linked to implementation and to accountability by management: it has to be practical and achievable. Indeed, it is often – perhaps more correctly – described as a target. The requirements of the two are, therefore, very different and to confuse them may weaken both processes. Here we focus on forecasting.

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11.1

THE FORECASTING PROCESS

A problem with the organisational dynamics of forecasting is the adaptation of forecasts to changed circumstances in the external environment. It is often assumed that forecasts are immutable: the annual forecast is to be renewed in 12 months’ time, and not a moment earlier; the five-year plan will only be replaced in five years’ time. It is felt to be an admission of failure if such forecasts have to be changed. This is understandable where these so-called forecasts are actually targets to which the organisation is committed. But, clearly, true forecasts (as opposed to budgets or targets) should be amended as and when the environment changes, since they merely report what is likely to happen without any intervention. Any change in the ‘budget’ on the other hand is likely to imply a change in the agreed strategy to address the future. The best managed organisations often have a regular (for example, quarterly) review of their annual forecasts (and associated budgets), so that forecasts for the remaining quarters can be based on the latest information. The most sophisticated organisations indulge in rolling forecasts whereby at each quarter a full year ahead is forecast – in other words, a new fourth quarter is added to the plan. This takes much of the drama out of the annual planning cycle, and means that there is no period in the year when the forecast may cover only a matter of days. This can happen, and often does, if the new annual forecast is only agreed in December, to cover January onwards! Even five-year forecasts may need to change quite dramatically each time they are reviewed, in this case typically on an annual basis. Over the preceding year the external environment, as well as the organisation’s own internal environment, will probably have changed significantly – and in ways that were not predicted. The overall economy will have changed direction; competitors will have changed strategies; consumers will have changed their tastes. The new five-year plan has to take all this into account. Admittedly, however, few organisations have the courage to make such major changes to their five-year plan – no matter how much these may be justified – since it smacks too much of a failure of their previous planning! The position may be quite different in the case of long-range planning, which may look decades ahead. The effort involved in such a process may mean that such forecasts are repeated less frequently, typically every three to four years. This is acceptable when the time-horizon is two decades or more – since any interim changes are unlikely to have a direct impact on the shorter-term budgets – although any major changes detected should, even so, prompt an immediate review.

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ACTIVITY 11.1 Consider how forecasting and budgeting are done in your organisation. (a) Is a distinction made between forecasts and budgets? (b) How frequently are the forecasts reviewed? (c) Are the assumptions underlying the forecasts made explicit?

COMMENTARY Your organisation may do genuine forecasting, but it is more likely to be one of the many that set themselves targets largely unrelated to any impartial view of future developments. Indeed, you may well find that these ‘budgets’ or ‘targets’ are more like wish-lists than rational strategies designed to address likely developments! Do not worry about this – most organisations struggle with long-range planning. Indeed, many do not attempt such activities, which is why this material may give you a valuable competitive advantage.

11.2

SHORT- AND MEDIUM-TERM FORECASTING

How then do you forecast the future? The starting point for most corporate forecasts is usually the past – and the relatively recent past at that. The next stage is to use your data to predict what will happen in the future – that is, to produce the forecast. The most traditional form, in business terms, is the numeric forecast associated with the annual budgetary procedures. Even so, such relatively short-term forecasts are notable for their unreliability. The evidence suggests that not only is their accuracy quite limited, but also many managers using them believe their accuracy to be much higher than it actually is or ever could be. Indeed, if the organisation is not to degenerate into anarchy, it must believe in and commit itself to the (short-term) budget/target figures. In Section 3 of Unit 1 we referred to the work of Makridakis, a leading expert in the field of forecasting. He emphasises the limitations of short- and medium-term forecasting – limitations which you may recognise intuitively even if you do not fully understand them (Makridakis, 1988). Nevertheless, you may be surprised by the range of errors that Makridakis identifies and which are summarised in Table 11.1.

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Table 11.1

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Biases in judgemental forecasting

Type of bias

Description of bias

Ways of reducing the negative impact of bias

Optimism, wishful thinking

People’s preferences for future outcomes affect their forecast of such outcomes

Have the forecasts made by a third, disinterested party Have more than one person make the forecasts independently

Inconsistency

Inability to apply the same decision criteria in similar situations

Formalise the decisionmaking process Create decision-making rules to be followed

Recency

The importance of the most recent events dominates those in the less recent past, which are downgraded or ignored

Realise that cycles exist and that not all ups and downs are permanent Consider the fundamental factors that affect the event of interest

Availability

Ease with which specific events can be recalled from memory

Present complete information Present information in a way that points out all sides of the situation to be considered

Anchoring

Predictions are unduly influenced by initial information, which is given more weight when forecasting

Start with objective forecasts Ask people to forecast in terms of changes from statistical forecasts and demand the reasons for doing so

Illusory correlations

Belief that patterns exist and/or two variables are causally related when this is not true

Verify statistical significance of patterns Model relationships, if possible, in terms of changes

Conservatism

Failure to change (or changing slowly) one’s own mind in light of new information/evidence

Monitor systematic changes and build procedures to take action when systematic changes are identified

Selective perception

Tendency to see problems in terms of one’s own background and experience

Ask people with different backgrounds and experience to prepare the forecasts independently

Regression effects

Persistent increases or decreases might be due to random reasons which, if true, would increase the chance of a change in trend

Explain that in the case of random errors the chances of a negative error increase when several positive ones have occurred.

11 FORECASTING

The test of any forecasting method is not how well it fits past data but how well it will predict future data. It is a feature of business, along with many of the social sciences, that new techniques are invariably tested and justified on historical data. In the natural sciences, on the other hand, tests (which are much easier to make since the variables are usually under the control of the experimenter) are almost invariably done in terms of the technique’s ability to predict new events (Popper, 1959). Unfortunately, this approach is a luxury not normally available to managers, since the complex uncertainties usually involved make business events much less predictable. Makridakis makes a plea for forecasting techniques to be kept as simple as possible since this means that managers are more likely to use the forecast. What he does not say, but is equally important, is that models which are easily understood are those in which the limitations are most obvious to the user – and deviations more immediately evident. His eventual recommendation is not that you should ignore forecasting (and hence long-range planning) but that you should understand the uncertainty involved. Uncertainty, classically where the probabilities are unknown, as opposed to risk, where they can be calculated, is a crucial factor to be taken into account in any long-range planning.

11.3

LONG-RANGE FORECASTING

Long-range forecasting – typically beyond 10 years – is even less certain than its short-range counterpart. This is reflected in the fact that most of the techniques are qualitative rather than quantitative. Even so, in view of the dangers which ignorance of adverse trends may pose, it is important that these views of the future be as useful and relevant as possible. Airliners now carry radar that warns of turbulence far enough ahead that they can avoid it and long-range forecasting fulfils a similar role for organisations! We are well used to handling ‘ordinary’ risks. Managers regularly come into contact with such risks and, indeed, this is one aspect of entrepreneurial activity managers are even supposed to relish. Where no probable pattern of outcomes can be derived from previous experience, however, the decisions involved become much more a matter of judgement. This is the area where scenario planning – the main practical focus of this material – can make a major contribution. Unknowables are, by definition, unknown, so we can do nothing about them in advance, except develop our ability to react rapidly to them. Based on his research with the OUBS Futures Observatory (Mercer, 1998a), David Mercer identified three categories of uncertainty. l

Hidden certainties. We should not lose sight of the fact that many future events – especially in terms of developments in the shape of markets – will result from obvious trends, and we have the planning tools we need to handle these. The classic example is demographic (population) trends, where, for instance, we can already say with virtual certainty that by 2025 OU BUSINESS SCHOOL

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today’s developing nations will account for more than 90% of the world’s population. In fact, many trends are currently hidden from us and are just as easy to deal with once we discern them. l

Expected outcomes. These depend on a range of social decisions which are not totally predictable but can reasonably be expected to occur. The growing power of individuals in general, and of women in particular, is just one example of the forces which affect political trends just as much as those in the market-place. These are based on consensual social decisions which, in effect, become self-fulfilling prophecies. This is where the use of scenarios, described in Section 12, becomes so important.

Random uncertainties. Finally, there are those genuinely random events – such as earthquakes or financial crashes – which, although small in number, can cause massive disruption to long-term plans.

ACTIVITY 11.2 Think back over the past five years or so to the external factors which – to the best of your knowledge – have had the greatest impact on the operations of your organisation. Which of the three categories above did each fall into?

COMMENTARY If your organisation has been unlucky enough to have run into one of the few random catastrophes which make fools of all of us forecasters, that will almost certainly have dominated the developments in your organisation. On the other hand – and with the benefit of hindsight – you can probably easily see the trends that were developing. Unfortunately, hindsight is one of the least useful talents. It is much more difficult to detect such trends before they hit us!

In practice, most long-range forecasts are ‘judgement decisions’ or – to put it more crudely – guesses but, it is hoped, informed guesses. In a small company it may be the owner who makes the judgement. In a larger organisation it may be the marketing manager. In the largest of all it may be the brand manager or even the manager of the forecasting department. Sometimes, particularly in the case of smaller organisations, it may be outside experts, who often publish opinions on what will happen in the future – which may then be closely followed by some readers. Indeed, one classical route to long-range forecasting has been to ask experts for their views about future developments. Where these experts are working in relatively narrow fields of technological development, this may have some validity. They are simply being asked to report

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on the developments in the pipeline: where such developments take many years to emerge there is plenty of time to observe their progress. If, on the other hand, the predictions are about social outcomes then the results are much less certain. The individual forecast, in this context, is inevitably a personal judgement. Almost all of the techniques of long-range forecasting, therefore, represent an attempt to make this process of individual judgement more accurate or, more often, to make it safer. In terms of accuracy, it is often suggested that working with several experts – rather than trusting the judgement of just one – adds to the accuracy. Indeed, David Mercer’s work (1995, 1997, 1998) suggests that – given the correct use of some of the techniques involved – the interaction between members of a suitably chosen group does seem to result in a better balanced view of the future, ‘richer’ in ideas than those created by individuals. More importantly, these interactions seem to broaden significantly the scope of such forecasts, unearthing discontinuities that individuals working alone might have missed. Paradoxically, however, our research also indicates that ‘experts’ are generally no better than ordinary managers in undertaking such long-range social forecasting. Indeed, they are sometimes significantly worse, since their desire not to put their reputations at risk sometimes makes them so conservative that they miss important new ideas. We shall look briefly at three methods of avoiding the supposed pitfalls of individual opinion: jury, focus groups and Delphi. These are the main techniques used by corporate planners in long-range forecasting.

Jury method This is the simplest of the formal ways of attempting to overcome the limitations of individual opinion. In essence, a panel of experts (or senior managers) is brought together in committee to pool members’ individual forecasts. Then, having agreed (or at least discussed) their individual cases, a corporate forecast emerges and is agreed. As the quality of the forecast depends on the quality of the participants, the jury should comprise the best possible team of relevant experts, those who are acknowledged to have the greatest subject expertise, from within the organisation and outside it. On the other hand, as we have already seen, such experts may not be the best, or the most imaginative, forecasters of social trends. Indeed, such a jury may dilute the expertise of the best forecasters on it; worse, it may follow the ideas of the most persuasive (or those with the highest status) rather than the most knowledgeable. The ways in which this can happen were described by Janis (1971) as ‘groupthink’ – a phenomenon of group behaviour with which you will be familiar. It refers to a situation in which a group of senior managers withdraw into what may be best described as a fantasy world. In this world they deny the unwelcome facts which OU BUSINESS SCHOOL

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face them, insisting instead on their invulnerability arising from superior morality – which their stereotyped opponents do not have – and maintained by psychological pressure on group members.

Self-documenting focus groups One answer to the problem of domination of juries by their more persuasive members is to adopt a variation of the focus group method traditionally used by market researchers. In this the participants in the process are put on an equal footing by a range of techniques, including the use of ‘Post-it Notes’ or their equivalent (to avoid verbal domination). This approach can work well for the development of scenarios and is described more fully in Section 12.

Delphi studies Perhaps the most famous attempt to overcome the limitations of individual judgement is the Delphi method of assessing the importance of identified events which may occur in the future. It was developed in the late 1960s by Helmer and Gordon of the American RAND Corporation. The participants remain anonymous, their identity known only to the study organiser. During the exercise, the participants respond to a questionnaire that is precisely defined: specific events are identified and the expert is asked to make a judgement of the probability of the event occurring before certain dates. For example, a question may be ‘What is the probability of a pill being developed that will extend the average life-span to greater than 100 years in or before the year 2000, 2005, 2010, 2020, 2050, or never?’ An expert in medical developments may respond:

Year:

2000

2005

2010

2020

2050

Never

Probability:

0.0

0.1

0.15

0.5

0.0

However, a second expert may respond:

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Year:

2000

2005

2010

2020

2050

Never

Probability:

0.0

0.1

0.4

0.5

0.9

0.0

11 FORECASTING

Some disagreement clearly exists between these experts. Now, if this were a discussion between them, the argument may be won by the most senior expert or the most aggressive expert or the most powerful expert. It is to avoid just this sort of bias that the Delphi method maintains anonymity among its participants. In order to allow adjustments, however, the results of the first round of responses are statistically analysed and fed back to all participants. Each can then see to what extent their own initial responses were close to or far from the mean. If they desire, they can then decide to modify their first response. Indeed, in some Delphi studies, those participants whose responses are far from the mean are asked to justify their opinion if they want to maintain it. Then others may be persuaded to shift their own responses. In this way, a wide range of opinion is captured in the first set of responses and amended in a second, or even a third, round of questions. Such an approach often leads to a narrowing of the range of responses and the final statistical analysis of the results can be a good guide to the eventual future outcome. This technique was very much in vogue during the 1970s. A significant shortcoming is that the assumptions often turn out to be heavily influenced by the prevailing fashions. One of the most interesting aspects of the work done by the University of Southern California, which conducted Delphi studies every year for two decades, was the light it shed on the ideas current at the time each of the previous forecasts was made (and perhaps rather less on the future!). The other main problem is that almost all experts have tended to be optimistic about time-scales. They nearly always expect changes to happen well in advance of when they actually do take place. On the other hand, the American Committee of the United Nations University have recently used the technique successfully – albeit as one element in a range of measures – in their global ‘Millennium Project’. ��

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12 HISTORICAL DEVELOPMENT OF SCENARIOS

HISTORICAL DEVELOPMENT OF SCENARIOS

So far we have looked at forecasting processes which lead to a single viewpoint – albeit in the form of a complex set of outcomes. We now move on to scenarios, which allow for uncertainty by examining a range of alternatives – and can also include the outputs from the other techniques as input – rather than limiting themselves to one single prediction.

12.1

THE EMERGENCE OF SCENARIO

ANALYSIS

The challenge facing strategic planners is how to deal with the uncertainty of the future. This has long been recognised as a problem in the strategy of warfare, and as long ago as the early nineteenth century the Prussian general and military writer Clausewitz dismissed modelling techniques as being suitable for tactics but not for strategy. In order to cope with the inherent uncertainties of warfare, he suggested ‘an educated guess and then gamble that the guess was correct’ (Herbig in Handel, 1989, quoted in Ringland, 1998, p. 11). The development of strategic planning over the past 50 years has been a search for ways to improve the quality of the guesses and reduce the gambles. The progress of this development in the immediate post-war years is shown in Figure 12.1. The Second World War had stimulated an enormous growth in interdisciplinary thinking, including social scientists as well as natural scientists and mathematicians. One area to emerge from this cross-fertilisation was corporate planning, built on game theory and decision analysis techniques developed initially for military purposes. This included the development by Hermann Kahn of the RAND Corporation of the technique of ‘future-now’ thinking, which combined analysis and imagination to produce a report as it might be written in the future. The term ‘scenario’ was used to describe this technique. When Kahn founded the Hudson Institute in the mid-1960s, he specialised in stories about the future to help people get out of mental traps and consider the unthinkable. In parallel with this, the Stanford Research Institute (SRI) was established as a think-tank to assist businesses with long-range planning (Ringland, 1998). Futures thinking developed strongly in the 1960s. The SRI produced a set of scenarios for the USA to the year 2000 (see Example 12.1), and also worked for the Environmental Protection Agency. Perhaps the most famous legacy of this period, however, was the Club of

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Rome model produced in 1970 to examine interconnections between population, supply and demand. It tracked five key variables – population, food production, industrial production, pollution and natural resources. The results of the modelling were to produce a picture of uncontrolled growth with its Malthusian consequences. This model was widely derided, as it appeared to ignore the self-correcting power of markets, but it undoubtedly succeeded in stimulating strategic debate! ��

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Figure 12.1

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Strands in the evolution of scenario planning (Source: Kleiner, 1996, in Ringland, 1998,

p. 13)

EXAMPLE 12.1 THE SRI SCENARIOS In 1968, the SRI was commissioned to create plausible scenarios for the USA to the year 2000 by the Office of Education, who wanted to envisage what sort of society it was educating children for. They developed five scenarios around two questions: 1 Would society be good at controlling its destiny, or not? 2 Would society be flexible, open and tolerant or would it be authoritarian, violent and efficient? The first scenario was the ‘official future’: status quo extended. This assumed that problems of population growth, dissent and ecological destruction would take care of themselves. The two questions gave rise to four other scenarios:

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Imprudent optimism, where bureaucracy and a raging recession met head-on.

Violence escalated, with terrorism increasing and schools taken over by street gangs.

Excessive reprivatisation, with unbridled capitalism leading to an efficient but divided society.

12 HISTORICAL DEVELOPMENT OF SCENARIOS

New society, which identified a society in which the consumer was no longer king and in which government adopted an ecological ethic.

The researchers believed the new society scenario to be the most desirable, but recognised that it could not happen under existing government and corporate structures. It was inconsistent with the values of the industrial society and predictably was rejected by the Nixon government. (Source: based on Ringland, 1998, pp. 14–15)

The sophisticated, centralised approach to corporate planning was epitomised by the planning models developed by General Electric. However, the rigidity and inadequacy of this approach was exposed by the series of shocks to the world economy that began with the oil price increases in 1973. This marked the emergence of scenario-based strategic thinking as an important counterweight to corporate planning. This approach was led – or at least popularised – by the Royal Dutch/Shell Group. Led by Pierre Wack, their planning work included consideration of discontinuities or fractures in the external environment – in other words, it built in the consequences of uncertainty. When oil prices quadrupled in the mid-1970s, Shell were prepared and gained a substantial – and sustained – competitive advantage. An outline of the Shell approach to scenarios is given in Example 12.2.

EXAMPLE 12.2 SCENARIO ANALYSIS IN SHELL In recognition that the oil industry required long-term investments, a study was set up in 1967 to look at Shell’s position to the year 2000. This showed that the predictable surprise-free environment would not continue, and that a shift in power from the oil companies to the oil producers could create major increases in the oil price. This was followed by a planning exercise involving senior managers world-wide. They were asked to consider the consequences if the oil price was not predictable: Having developed this scenario, the problem was how to get into Shell’s corporate culture the idea that it had a blind side. ... So we produced a set of absolutely impersonal, mechanical scenarios about the future of oil prices. One of them was the general perception of the corporation, that prices would remain as they were. And one expressed the idea – our feeling, but not presented as such – that oil prices would rise. Even this dispassionate presentation of the idea had a traumatic effect. (Newland quoted in Ringland, 1998, p. 20)

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Subsequent events lent credibility to the scenario process, and work in this area continued in the 1980s. In 1983, we presented the Royal Dutch/Shell managing directors with two scenarios: one called Incrementalism and the other called the Greening of Russia. By that time, we knew enough about the Soviet Government to say that if a virtually unknown man named Gorbachev came to power, you’d see massive economic and political restructuring; an opening of the West; arms control; declining tensions in the West; and major shifts in international relationships. It was not that Gorbachev, as an individual, would cause the changes. Rather, his arrival in power would be a symptom of the same underlying causes. (Schwartz, 1991, p. 58)

Although not all the scenarios were equally prescient, from the late 1980s scenario planning became more closely integrated into the strategic planning systems, and was extended to make it more meaningful to line managers. (Based on Ringland, 1998)

Scenario analysis is now a widely used technique in the development of corporate and marketing strategies in a range of organisations. Next we identify some of the ways in which it can contribute to marketing planning.

12.2

THE USE OF SCENARIOS

So far we have concentrated on the limitations of conventional approaches to forecasting by demonstrating how inadequate they are when the external environment is uncertain. By basing forecasts on extrapolation of the past, they implicitly view the world as essentially stable, whereas the environmental reality for marketing managers is full of unexpected changes and discontinuities. Now we shall look briefly at some of the ways in which scenarios can help overcome these limitations. As we have seen, scenario analysis began as an academic technique. However, most of its uses today have grown out of practice and have been developed by managers and consultants. Whereas traditional forecasting seeks a single truth and representation of reality, the scenario method encourages enquiry and ‘courts contradiction and paradox’ (Schoemaker, 1993, p. 194): Most companies are designed to process one scenario only, the official corporate future. Risk analysis is permitted within the assumptions of this future, but not outside. Furthermore, budgets are normally based on single-point estimates of growth rates, exchange rates, costs, etc. ... It is not surprising that the scenario method flourished first in industries with long lead-times, highly specific assets, and great

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external uncertainty [such as oil, transport and utilities]. ... It is precisely because scenarios do not aim to predict the future, but rather bound it, that a consensus building approach can work even if faced with starkly different viewpoints. ... In our age of information overload, the ability to distinguish signal from noise is a critical skill. But, incisive pattern recognition can only occur if the right mental maps exist in a manager’s mind. Scenarios can develop, align and focus such maps. (Schoemaker, 1993, pp. 199, 200)

This concept of mental maps is emphasised by Bood and Postma (1997), when they consider the limitations under which most managers work when they engage in strategic planning: Above all, as ordinary human beings, managers are not the rational, all-overlooking actors some strategic management theorists wanted us to believe for a long time. Managers often do not define and explore strategic problems properly and do not systematically generate alternatives. Instead they only partially study one or two alternatives that either are readily available or already favoured for a long time. After they have made a choice, they often stick firmly to their initial choices even if altered circumstances require modifications. Last, but not least, they base their interpretation of the outcome on its desirability. Being ordinary people, managers work within cognitive limits. Whether we like it or not we are all severely constrained in our possibilities to cope adequately with the host of different stimuli and data we are confronted with daily. Cognitive psychology teaches us that, in order to be able to act, people construe simplified mental images of the world they live in and impose these images upon the world around them. These images function as a frame of reference for action and interpretation of the world and the data it produces. The images are the world as seen through our eyes. General ways in which people cognitively simplify stimuli and data are well-known as heuristics or rules of thumb. Examples include ‘wishful thinking’, ‘availability’ and ‘selective perception’. (Bood and Postma, 1997, pp. 636–7)

They argue that these mental models limit the ability of managers to envision the future, restricting them to only a narrow range of future possibilities (Figure 12.2). Mental models are shaped by social and cultural background, by education and experience. However, they can be changed as people learn from their experience. Scenarios contribute to this learning by encouraging managers to explore unfamiliar areas and by enabling them to analyse these areas through ‘What if ...?’ models. This stretching of managers’ mental models is probably the greatest benefit that scenario analysis can bring to marketing and corporate strategy, but it is not the only one. Bood and Postma (1997) identify five other functions of scenario analysis. l

Evaluation and selection of strategies. Scenarios provide a framework within which managers can judge alternative strategies. OU BUSINESS SCHOOL

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Figure 12.2

Limited imaginative faculty (Source: Bood and Postma, 1997,

p. 637) l

Integration of future-orientated data. Scenarios can integrate qualitative as well as quantitative inputs, including soft and ‘fuzzy’ variables, and they can incorporate results from other forecasting techniques.

Exploration of the future. By formulating hypothetical sequences of events, scenarios can help to identify major changes and potential problems, and to generate strategic options to deal with them. This allows for anticipation of the unexpected and provides for an early warning system.

Awareness of environmental uncertainties. Scenarios require managers to accept uncertainty, try to understand it and make it part of their reasoning (Wack, 1985).

Organisational learning. Scenarios provide test-beds for managers to explore the consequences of strategic decisions. In real life, considerable time elapses between a strategic decision and the results of its implementation. This feedback lag extends and delays the learning process. Scenarios offer a context for managers to rehearse strategic decisions and think over their consequences.

To complement this academic analysis of the benefits of using scenarios, we conclude this section by indicating some of the insights that scenarios can bring to practising marketing managers by providing answers – or at least guidance – to the following questions (Millett, 1988).

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Is demand for products or services likely to expand or decline?

Are market conditions becoming more or less favourable?

Are there opportunities for new products or services?

Are there emerging technologies that will affect operational processes?

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Is there likely to be significant change in the competitive scene?

Will a high degree of flexibility be necessary to cope with the level of strategic uncertainty?

This checklist will be helpful as we move into the practicalities of scenario analysis.

12.3

A SIMPLE APPROACH TO SCENARIOS

Scenario forecasting was originally conceived and developed as a highly sophisticated process. Many of the examples that we have referred to already have heroic themes and require inputs from large teams, drawing on many disciplines and being honed through multiple iterations. There is – and will continue to be – a need for such analyses, the results of which are interesting and useful to a wide range of organisations. We hope that your interest will by now have been sufficiently aroused for you to follow and read these views of the global future. We give some examples in the appendices to this unit. For most managers, however, their problems are rather more prosaic. While they may be interested in the future of the world, its people and all that, their real concerns relate to their own organisation and its survival and success over the next 10 years. For the rest of this unit, therefore, we shall discuss a simple approach to scenario analysis that can be used by most managers and will be relevant to most organisations. It involves working in groups but it does not require outside consultants or facilitators. Its equipment needs are limited to flip-charts and an endless supply of ‘Post-it Notes’. It is an approach that we have developed and used in a wide range of circumstances, and we are confident that it is robust (Mercer, 1995a and b). The most important message to emerge from this work is that scenarios can be simple. In our experience, the simpler they are – and the simpler the process used to derive them – the more effective they may be, not least because those using them can understand how they work. Even Shell, which is reportedly the world’s leading commercial user of scenario forecasting, now uses relatively simple techniques to create its scenarios – techniques that are far removed from the academic sophistication of earlier times. The process consists of three stages: l

environmental analysis

preparation of scenarios

strategy formulation.

In the next section we address the first of these stages: environmental analysis. ��

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ANALYSING THE ENVIRONMENT

Scenarios can only be as good as the information on which they are based. For this reason, the environmental analysis – which can follow any of the paths described in Unit 1 – must be of as high a quality as possible. Even so – and mindful of the limited resources available within most organisations – it will probably not justify an excessive level of sophistication; and, fortunately, our experience again indicates that such sophistication is rarely needed. Thus, our practical advice is simply to cultivate a deep curiosity about the external environment; and to maintain maximum exposure to the widest range of media, as changes often show up in subject areas far from your day-to-day business. You are also recommended to develop an informed viewpoint, which will improve your chances of recognising early signs of change, no matter from which direction they are coming. We have found that almost all managers use general reading as the main source of their analysis, combined with the more specific information from the industry and specialist press they read as a normal part of their work (Mercer, 1996). You have probably already assimilated the type of information required for environmental scenarios from your general and specialist reading; which is why we stress the value of reading as wide a range of material as possible. Typically, therefore, you need to bring to the process no more than your existing knowledge. There seems to be no special expertise needed to detect signals. The best advice, however, is to analyse the external environment as part of a team. If nothing else, more of the environment will be analysed – but it also seems to have a deeper effect: to amplify the early signs of change, and to develop resonances as team members interact with each other. We generally recommend the use of teams of between six and eight participants, but there may be 10 participants if they are used to working together regularly. If regular face-to-face meetings are not possible, we strongly recommend the use of computer conferencing to develop an ongoing debate between team members. Although such an approach is not mandatory, we have found that, in general, teambased scenarios are noticeably better – and certainly reflect wider perspectives – than those developed by individuals. Although it is worthwhile producing scenarios with just the group of people who already work together, this may pose the problem that they share the same mind-set and use similar mental models. It is preferable, therefore, to involve as diverse a group as possible

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Figure 13.1

Environmental analysis

13.1

and maybe to use several such groups. Each group should contain people from across the organisation as a whole, so that the widest range of experience can be drawn upon. It is also a good idea to involve one or two ‘reference’ outsiders, that is individuals who can be trusted to introduce a different perspective. The steps in environmental analysis are shown in Figure 13.1.

STEP 1: ENVIRONMENTAL ANALYSIS

The environmental analysis should be done in two steps. First, it is necessary to brainstorm – to think openly and divergently about the external environment in which your organisation is operating. Your group – between six and ten people is ideal – should encompass a range of functional or disciplinary outlooks, and it should include at least one participant who is willing to challenge the status quo. You should be clear among yourselves what time-scale you are operating in – we would recommend 10 years for most organisations. Remember – the key to brainstorming is that it must not be judgemental. You should be willing to put up wild ideas, and you should not be afraid to think the unthinkable. Strategic thinking is far more likely to suffer from too narrow than too wide a range of ideas. You should encourage yourself to be stimulated by what your colleagues have suggested, and by what you have heard or read as a result of your general scanning (Aguilar’s ‘undirected viewing’ – see Unit 1). The wider your interests and reading, the more valuable your contribution to the brainstorming is likely to be. For this reason, you may find it helpful to hold more than one brainstorming session – to allow some of the ideas to ‘ferment’. You may find the STEEP framework helpful as a checklist or a series of prompts, to ensure that you are not missing whole large areas of external influence. However, you should not follow it rigidly, and you certainly should not aim to get a similar number of ideas under each heading.

13.2

STEP 2: IDENTIFY CLUSTERS

The second stage of the environmental analysis is to combine the ideas of the group into clusters. This must not be a judgemental or an analytical process: you are not trying to weed out or modify anyone’s ideas but to group them in order to make the next stage manageable. Although there are no rules, we would expect you to end up with about five to ten clusters of related ideas. These will then form the drivers that you consider in the next stage – scenario preparation. If there are any ideas that are so wild they 120

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do not fit into any clusters, do not discard them but store them in a separate list. You should manage the time carefully, as it is important to ensure that the participants neither get bored nor feel unduly pressured. In our experience, an hour is about the most that people can contribute constructively in a brainstorming session. If ideas are still flowing freely after an hour, you may find it useful to schedule a second session. The second stage, of forming the clusters, can be difficult but you should not allow it to extend indefinitely. We suggest that you plan for half an hour and should be willing to extend it to an hour, but not beyond. In order to illustrate how scenario analysis can be used in practice, we shall use a case study of its application in a commercial organisation. The first two steps of the case study – the environmental analysis – are described in Example 13.1.

EXAMPLE 13.1 CASE STUDY: STEPS 1 AND 2 The company was involved in the research, development and marketing of products for agriculture: fertilizers, pesticides and seeds. It had shown strong organic growth through the 1970s and 1980s, based on successful R&D and an aggressive growthorientated production and marketing strategy. In 1987 it completed the acquisition of a major US manufacturer. As a result it was one of the top three companies in its sector, and probably the most profitable. West Europe and the USA each accounted for about 40% of its sales, and a higher proportion of its profits. After the acquisition, it initiated a fundamental long-term review of its strategy. This resulted in a number of scenarios being presented to the Board for consideration. This case study is an abbreviated description of the process and outcome. Again, it is worth emphasising that although this planning process happened in 1990 it offers a good, and easy to understand, example of the scenario forecasting process in general.

Step 1 Environmental analysis This was done over a period of several months. It was led by head-office staff but it involved most of the company’s 40+ overseas subsidiaries. The process was informal, and the ideas were brought together through a series of meetings; the meeting notes were widely circulated for comment.

Step 2 Identify drivers for change/produce change clusters The following were identified. l

Food surpluses and the growing cost of the European Common Agricultural Policy. This could lead to the reduction of agricultural production and prices and so to declining farm incomes.

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Accelerating population growth in the Developing World, leading to increased demand for food products and therefore for agricultural inputs.

Increasing agricultural production in East Europe as a result of improved management and technology, leading to pressure on prices in West Europe (weak signal – see Unit 1).

Continuing improvements in agricultural technology, leading to further increases in productivity and higher use of agricultural inputs.

Improved dissemination of existing technology to the Developing World, leading to growth in demand for ‘old’ technology in preference to new innovations.

A change in the patent law in Europe resulting in reduced periods of protection and the growth of generic manufacture of out-of-patent products, so reducing the returns on investment in R&D (weak signal).

The emergence of biotechnology and genetic engineering from an academic to a practical science, thereby revolutionising both invention and testing of new molecules (weak signal).

Changes in consumption habits leading to a strong growth in demand for ‘organic’ produce, so depressing demand for agricultural inputs (weak signal).

Greatly increased environmental regulations resulting in the pricing out of conventional chemical manufacture.

The migration of chemical manufacture to the Developing World (weak signal).

Many of these concepts stimulated furious debate, in particular: l

Production managers were unwilling to countenance any likelihood that conventional manufacturing processes and location would come under threat.

R&D managers dismissed as laughable the prospect that biotechnology would have any impact on chemical science.

Marketing managers, especially those working for overseas subsidiaries, refused to accept that either the growth or profitability of their business was likely to come under any threat.

Having discussed environmental analysis, we now move on to discuss the preparation of scenarios. ��

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PREPARING SCENARIOS

Whereas Step 1 – environmental analysis – requires divergent thinking, the preparation of scenarios is a convergent process. It is logical and analytical, and it requires a degree of intellectual rigour. It begins with the main external drivers that have been identified in the environmental analysis, and develops what could be the consequences of these drivers on the future development of the organisation. There are several ways in which this stage can be progressed: we suggest that it is done in four steps, as shown in Figure 14.1.

14.1

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Figure 14.1

Preparing scenarios

STEP 3: SELECT DRIVERS AND DEVELOP FRAMEWORKS

The next step – selecting the drivers and developing a logical, causal framework – can be difficult. It is the step many groups find the most difficult. The intention is to select from among the clusters of drivers those that seem to be the most strategically important, and to explore the consequences for the organisation of these events taking place. For example, if your organisation trades internationally, a significant change in the exchange rate would be strategically important. It would be sensible, therefore, to think through the consequences of this event – the change in the competitive scene, the impact on profitability, the effect on location of production, etc. Two techniques can help in selecting the key drivers. Because management time is limited, it is essential that only a limited number of drivers are selected – the 80:20 rule applies here as in so much of management. The drivers selected must also be genuinely variable and have significant alternative outcomes. Factors whose outcomes are predictable should be included in all scenarios, and your efforts should concentrate on those that are both uncertain and important. The importance/uncertainty matrix can help in making this selection (see Figure 14.2). Thus, only those drivers (here topics 5 and 6) in the top right-hand quadrant (important and uncertain) should form the basis for different scenarios. Drivers in the bottom right-hand quadrant (topic 2, and especially topic 1) are important and should be treated the same in all scenarios. On the other hand, drivers in both the left-hand quadrants (topics 3 and 4) should be ruthlessly discarded as merely distractions from the planning process. Note, OU BUSINESS SCHOOL

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Figure 14.2

Importance/uncertainty matrix

though, that their importance should be judged in terms of what is important for the organisation, not for the individual manager. The second technique is to differentiate between factors that affect the industry segment as a whole and those that affect the different competitors or players selectively. Both sets of factors are important but those with a differential competitive effect are especially so – they will create circumstances of competitive threat or opportunity. If you consider these two techniques together, you should select external drivers that are: l

potentially important

uncertain or unpredictable

likely to have a competitive impact.

The example of a significant shift in the exchange rate potentially meets all of these criteria – it will affect competitors differently if their cost bases are in different currencies.

ACTIVITY 14.1 Plot the pharmaceutical industry drivers that you identified in Activity 13.1 on an importance/uncertainty matrix (as in Figure 14.2). Identify any drivers in the important/uncertain quadrant that may affect pharmaceutical companies differentially.

COMMENTARY While most of your drivers are important, only some of them will probably also be uncertain. You may decide, for example, that changing demographic patterns, growth in demand for treatment and continued financial pressure on health services are all important but also predictable. They will therefore be part of any future strategic plan, and are not differentiators – they should not therefore be key drivers for your scenario analysis.

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The second part of Step 3 is to develop logical frameworks around the selected drivers. This is where a combination of creativity and analytical rigour is necessary. Your task is to explore the consequences of each of your selected drivers. You should be addressing a series of questions of the type: â&#x20AC;&#x2DC;If X were to occur what would be the consequences?â&#x20AC;&#x2122; For the earlier example of a shift in the exchange rate, we could argue as follows. The home country currency strengthens, leading to: l

increased competition and loss of market share in the domestic market, and an increase in research, development and manufacturing cost base resulting in declining profitability and vulnerability to hostile takeover

OR l

relocation of manufacturing overseas in a low-cost economy

OR l

sustained investment in production technology in selected sites, resulting in reduced costs but many site closures and lay-offs and massive labour and community hostility.

As you work through the drivers, they will tend to merge and overlap. So, what begins clearly as an economic driver soon develops social, technological and political consequences. It is these interactions between drivers that become the threads of the scenarios that you identify and develop in the next two steps. As we have said, these frameworks should combine logic and imagination. One technique that can help in this is event strings. The movement through time into the future can follow a complex path. Intermediary events will take place, which can be identified. A sequence of these will then lead to a particular outcome. The ability to draw and understand event strings can contribute greatly to the strategic value of scenario analysis. Event strings can be constructed in various ways: l

Participants identify the outcome that they are modelling (e.g. hostile takeover in the example above) and then develop the logical chain or string of events that led to that outcome.

The second approach is to start from the present, to identify key events in the future and to plot the consequences according to the outcome of the event. This is analogous to a decision tree. If these strings are drawn for a series of drivers, it is then possible to plot routes through them, which form the bases of the subsequent scenarios.

The use of event strings is illustrated in Example 14.1, which describes Steps 3 and 4 in the case study.

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EXAMPLE 14.1 CASE STUDY: STEPS 3 AND 4 Steps 3 and 4 Select drivers and identify scenarios This marked the switch from the divergent to the convergent approach, which was discussed within the Planning Group. There was general agreement that political support for agriculture in Europe would decline and that concern about the safety of chemicals would grow. There was disagreement about the possibility of technological change, but it was agreed to model it. Several alternative futures were considered but only two were taken forward to the next stage (see event strings A and B in the figure opposite).

A range of other techniques are used by consultants and others to establish the logic framework for scenario development. These include forcing scenarios, as described in Example 14.2. However, these are neither appropriate nor necessary for most managers, and we recommend that Step 4 should focus on the logical development of the key drivers identified in Step 3.

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EXAMPLE 14.2 FORCING SCENARIOS Consultants frequently recommend the use of forcing scenarios – devices intended to force outcomes based on advice from the consultant’s own experts. These devices may, for example, be fourbox matrices derived from the two or more dimensions that have emerged during the process of examining the uncertainties. This type of approach can become very complicated. The Batelle Institute of Columbus, Ohio, for example, assigns three levels to each factor before using a cross-impact matrix to resolve them. At a simpler level, in the mid-1990s British Airways used the two dimensions of growth (a crucial feature of their market) and governance (regulation/deregulation around the world) as the basis for their scenarios. They then focused on just two outcomes: ‘Wild Gardens’, which had growth as its main focus; and ‘New Structures’, which was dominated by issues of regulation. (Source: based on Idon, 1997 and Moyer, 1997)

14.2

STEP 4: IDENTIFY SCENARIOS

If Step 3 has been completed fully, you will have a choice of interconnected events with a range of potential outcomes. You will need to select some of them to develop into full scenarios. There will probably be considerable debate about the choice of scenarios. As you try to reach consensus, you should take the following points into account. l

There is no ideal number. We recommend identifying between two and four scenarios. You may find that time pressures limit you to two. It is impractical to go beyond four, as most people simply cannot cope with a larger number of versions.

Scenarios are not intended to be predictions, so you should not select them on the basis of probability.

Effective scenarios will combine more than one driver, and will explore and develop the interaction of external events.

You should try to avoid scenarios that are simply optimistic or pessimistic views of the future. Scenarios should be built on key external drivers, and it is the organisation’s response to these drivers that will determine whether the events are favourable or adverse.

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The chosen scenarios should be complementary – they should reinforce each other by looking at the future from different perspectives. This should avoid managers choosing just one preferred scenario, and lapsing back into single-track forecasting. The two scenarios should be equally likely, or unlikely, and they should not be obvious opposites. Managers should be discouraged from choosing a favoured scenario, and it is important to give them neutral titles. For example, Shell used the

titles ‘Sustainable World’ (based on

severe environmental legislation) and

‘Global Mercantilism’ (based on price

wars) when reviewing their future in the

early 1990s (Kahane, 1992).

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Scenarios should be selected so that they are stimulating and provocative. However, they must not be so wild that they are dismissed as ridiculous, or the Figure 14.3 Stretching but plausible scenarios process will lose credibility. Good scenarios should be stretching but plausible. If a probability distribution of events were to be plotted, the selected scenarios should be towards the extremes but still on the curve, as in Figure 14.3.

ACTIVITY 14.2 Reflect on the planning assumptions that are used by your organisation, and attempt to answer the following questions. (a) What time-scale do they cover? (b) Is this time-scale appropriate for the investment and development decisions that the organisation is required to make? (c) To what extent do they allow for important changes in the organisation’s external environment? (d) Can you identify sets of events that meet the definition of ‘stretching but plausible’ that might apply to your organisation over a 10-year period but are not reflected in the current planning assumptions?

COMMENTARY It is relatively unusual for an organisation to make explicit planning assumptions for a period as long as 10 years. If it does, the assumptions for Year 10 will frequently be very similar to those for Year 5. There are some sectors where a planning horizon of 10 years is not helpful because the external environment is either very stable or – more commonly – highly unstable. Examples of very stable environments have, in the past, included sectors such as universities, public utilities, the armed forces, local and central government and the police. Today, however, even organisations in these traditionally slowchanging sectors probably expect to face substantial change over a 10-year period. It is the other extreme that is more common today – sectors where the external environment is changing so rapidly that long-term planning is inconceivable. Many high-technology industries fall into this category – which implies that they should pursue only relatively short-term investments and developments.

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14.3

STEP 5: WRITE THE SCENARIOS

Scenarios should be written in the most suitable form – there is no fixed format. The rule is that in general you should produce scenarios in the form most appropriate for the managers who are going to use them for setting strategy. In other words, you should match the format to the interests and needs of the target audience – a good marketing approach! The purpose of the scenarios is to challenge and stimulate strategic thinking, and they should therefore be interesting and provocative. Scenarios are usually in the form of words rather than numbers as the analyses on which they are based will be largely qualitative. It is important to give each scenario a title, which should be both descriptive and catchy, as it is likely to be used in discussion as a shorthand for a complex set of interrelated ideas. Many managers will use conventional report formats to present scenarios, but there are real benefits from using more imaginative approaches. The technique of ‘future history’ – in which the scenario is written as if in the future, describing the events of the previous 10 years – can be particularly effective. The future history approach as used in the case study is described in Example 14.3.

EXAMPLE 14.3 CASE STUDY: STEP 5 Two alternative scenarios were then written, based on the two event strings. The time-horizon was the year 2000 (remember that this happened in 1990).

Scenario A Long-term exit The first Board Meeting of the new millennium was a sombre affair. There were two main items on the agenda: an investment proposal for replacing the main production plant and a review of the preliminary trading results for 1989. The trading results were dreadful. Although still making a profit, the return on capital employed had declined to 2%, and like-for-like sales had shown no growth on the previous year. Indeed, the only reason why the results were not in the red was the success with the very large government tender in Iran – and this could hardly be relied on in future years! The business was still generating cash, largely as a result of slashing R&D expenditure over the past five years and cutting back the capital budget. But the production director now argued that investment of over £40 million was needed in the main production plant if serious safety problems were to be avoided – the plant was now more than 20 years old and maintenance expenditure had been kept to a minimum over the past few years. All the directors knew that the business simply could not support an investment of this size.

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The problems had begun in the late 1980s and, as always, were political in nature. After reunification, the German government was determined to bring the rest of the ex-Soviet bloc countries into the European Union. It was strongly supported in this by the other northern members, although Britain remained ambivalent, as usual. Once this expansion was agreed in principle, it soon became apparent that the cosy system of cross-subsidies that had kept the original members happy for so long could no longer be afforded. Changes would have to be made, and nowhere would these be more pronounced than in the Common Agricultural Policy. The proposal to reduce subsidies over 10 years until farm prices were at world levels was met with predictable outrage by the small farmers of central and southern Europe, but the momentum for change was too strong – and the rural vote was becoming less important! The impact on the industry was immediate and inexorable. Investment in farming dropped like a stone – the agricultural machinery industry had all but disappeared in five years. Farmers became increasingly wary of purchasing expensive high-yielding seed, and with this the need for pesticides also declined. This first manifested itself as pressure on prices – it was impossible to continue to move prices up at twice the rate of inflation. Dealers also became adept at moving products around within the EU – so higher prices could no longer be charged in the north than in the south. Soon demand also began to fall as farmers reverted to less intensive technology. The real blow, however, was the failure of their blockbuster product in the early 1990s: the third-generation fungicide was expected to revolutionise cereal production in northern and central Europe as its predecessors had done. But farmer interest was muted, and the company was faced with the ignominy of having to discount prices in its launch year. The stateof-the-art factory had never operated at more than 40% capacity and the product would never now recover its £200 million development costs. Events outside Europe were little better. The ending of state control in East Europe meant that food production rose rapidly, and China also became a net exporter of agricultural products. Output in North America had remained static, reflecting the growth in world surpluses and the decline in demand for its exports. So what had been one of the strongest growth industries of the post war era had become, almost overnight it seemed, a mature, declining business with few prospects for growth and little scope for new technologies. If only they had seen the signs in the 1980s, in time to adjust to the new realities of a world with a structural food surplus and declining political commitment to domestic agriculture.

Scenario B Brave new world The first Board Meeting of the new millennium promised to be an exciting affair. ‘Blood all over the walls’ was the confident prediction. The problem – if problem it was – was one of

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success: success with the policy of investment in biotechnology that the company had committed to in the late 1980s. The issue facing the Board today arose directly from this – it was the proposal to close down the company’s conventional research station network, and concentrate instead on its world-wide development facilities. The proposal was based on the expectation that new products would emerge from the network of small biotechnology laboratories with which the company was involved. The Research Director was expected to fight to the last breath to protect the huge team of organic chemists and support scientists that he had built up over the past 20 years in the company’s three research stations. The seeds of the present dilemma had been sown by a decision made back in 1988. One of the Section Heads in the Chemistry Department, a relatively young scientist who had not been in the company very long, proposed that the company should put a small amount of capital into a new research laboratory being established in the local science park. He argued that this would be a cheap way for the company to keep abreast of some of the new ideas in this field coming out of universities. He was supported in this by the Head of Corporate Planning, who was becoming increasingly concerned about the impact that biotechnology might have on the company’s business, which was based on conventional chemistry. He did not personally believe that biotechnology would lead to commercially viable business, but if it did the impact on the company could be huge. He therefore saw the proposal to invest in one of these fashionable ‘boutique’ laboratories as a cheap form of insurance. So it proved. Not only did the company find that it had access to new scientific thinking but also it had an option to license any products emerging from the laboratory. This coincided with a lull in the new products emerging from its conventional research programme, and over the next four years the company took on three new products from the external laboratory for evaluation. None of them had particularly large market potential but the science was undoubtedly interesting. Over the same period the company found that large amounts of venture capital were moving into biotechnology, and that the value of its share in the laboratory had grown 20-fold. Moreover, in the periodic discussions with city analysts, questions increasingly focused on the company’s involvement in biotechnology. Encouraged, the company sought out other similar start-up ventures, and was gratified to find that in most cases its relatively small capital outlay soon grew, and the number of small but interesting new products in evaluation increased steadily. In parallel, the company set up its own in-house biotechnology unit, which attracted bright young scientists coming out of university. Soon the company acquired a reputation as one of the leaders of the revolution in molecular chemistry and genetic engineering.

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All this had been made possible by the solid base of established products that were being marketed internationally. Although not showing much growth, these products pumped out cash during the 1990s, which was used to fund the expansion in biotechnology research. By the end of the decade, none of the new products had yet reached the market – it took an average of 10 years to bring a new product to market, and a couple of the early leads had been dropped for reasons of poor performance. The company’s share price continued to soar, but its cash flow became less healthy as the older products began to run out of steam. Throughout this period, the Research Director had been happy to support the new biotechnology research but had insisted that the conventional chemistry was continued. He remained sceptical about the long-term commercial worth of biotechnology, although as attracted as any by the science itself, and so had retained his large team of organic chemists doing conventional research at the company’s three stations in the UK, USA and Japan. By the end of the decade, however, it became clear that the company no longer had the resources to fund both long-term research programmes while still maintaining a world-wide development capacity. An important decision therefore needed to be made, but the company was in a strong position – and most of its competitors envied it its dilemma!

14.4

STEP 6: IDENTIFY KEY ISSUES

The final step in the preparation of scenarios is to identify key issues. This requires examination of the scenarios to determine what are the most critical stages or events – the key issues or branching points. These will relate to events that will have the greatest impact on the long-term future of the organisation – and the potential to generate crises. These branching or turning points are the events or decisions that will determine the long-term survival of the organisation. Different decisions on these key issues will lead to quite different outcomes, affecting the entire future of the organisation. Identification of the key issues that determine these turning points is the starting point for the production of ‘robust strategies’. The key issues in the case study are identified in the next activity.

ACTIVITY 14.3 Based on your reading of the case study so far, what would you identify as the key issues or turning points for the company over the next 10 years? Compare your answer with the brief analysis below.

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COMMENTARY Case study: Step 6 The scenarios of the future in 2000 were presented to the Board in 1990 and a lively debate ensued! When the protagonists of the various views had fought themselves to a standstill, the Head of Corporate Planning concluded that there seemed to be a consensus that the future of their industry would be very different from the past. However, there was no agreement on the shape of the future. He suggested that the scenario analysis had indicated there were four key external factors that could change fundamentally the nature of the industry and the competitive strengths of the key players: l

the future of the Common Agricultural Policy

the revival of agriculture in East Europe and China

the emergence of biotechnology

a reduction in the period of protection granted under a patent.

It was agreed that the Head of Corporate Planning should monitor these areas closely and commission some expert work to improve the company’s understanding of these key factors.

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FORMULATING STRATEGY

Scenarios are, of course, only a means to an end. They identify the long-term forces, and consequent events, which the organisation’s long-range planning must address. They are successful only to the extent that they lead to effective strategy formulation. This is the final stage of scenario analysis, as illustrated in Figure 15.1.

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Thus, the final stage is to match the organisation’s limited internal resources to the external challenges that it faces. The special contribution of scenario planning is to allow, and indeed encourage, the development of a robust set of strategies. These strategies should protect, as far as possible, against all the major threats potentially facing the organisation, and they should exploit the most important opportunities open to it.

Figure 15.1 formulation

Strategy

The purpose of scenarios, therefore, is to ensure that as many as possible of the long-term threats and opportunities facing the organisation are identified and addressed. The experience of Shell has demonstrated several times how such an awareness of the alternatives facing it has enabled it not just to handle changed market conditions but to take advantage of them (Wack, 1985; Kahane, 1992). We now move on to examine how you develop, and implement, strategies that will be robust under conditions of external volatility. The purpose of many approaches to traditional (short-term) marketing is the production of the marketing plan. This will determine the specific activities for the next year but it does not usually go beyond that to look at the longer term. Even then, not all managers base their day-to-day decisions on the contents of the marketing plan, even though they may have been involved in its preparation. The reasons for this are eminently practical – their actions are, quite understandably, dictated by the reality revealed by subsequent events rather than the historical theory contained in the plan. But there is also a psychological aspect. As Bernard Taylor says: ‘Planning is an unnatural process. It is much more fun to do something’ (Taylor, 1986, p. 15). John Preston went further: ‘The nicest thing about not planning is that failure comes as a complete surprise, and is not preceded by a period of worry and depression’ (quoted in Taylor, 1986, p. 16). On the other hand, long-range marketing plans really must provide a basis for decision making. This is because most line managers do not have the expertise to understand the long-term implications of the operational decisions they make. OU BUSINESS SCHOOL

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The traditional approach to strategy formulation is that the shortterm marketing plan feeds into the corporate plan and is the sole marketing input to strategic plans. However, our experience suggests that it is not merely preferable but essential to do a separate review of the far future. Without this, there is a risk that the overall process will be swamped by the shorter-term considerations (and especially the problems) that tend to dominate the main debates about strategy. The preparation of a long-term marketing plan is therefore an essential element of effective strategic planning. We suggest a fivestage approach, summarised by the mnemonic PROACT, which is explained in Figure 15.2. ��

Figure 15.2

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This somewhat prescriptive approach has been found to work well in a range of organisations (Mercer, 1998d). Each stage is within the capabilities of most managers and, therefore, presents little threat and should pose few problems.

Isolate turning points. As we have seen, the starting point must be a definitive Five stages of strategy formulation statement, ideally a formal map of some kind, of the issues or turning points identified in the scenario analysis. These are the issues that will decide the long-term future of your organisation and its markets. This is arguably the most important step of all, and the one where most organisations fail. If you cannot identify what factors will determine your fate, you will not be able to create the most effective robust strategies. l

Decide robust strategies. You then need to develop a set of marketing strategies to protect against (or to capitalise on) what has emerged from the previous step – in terms of the key turning points. These strategies may take the form of commitment to certain products, markets or processes, or they may take the form of contingency actions to prepare for future eventualities. They are likely to cover a wide range of possibilities, and will be informal and qualitative, unlike the more formal and quantified documentation underpinning conventional corporate strategies.

Test against corporate strategy. This stage requires a degree of self-confidence! It is to map these long-term robust strategies on to the shorter-term corporate strategy which is produced through the conventional corporate planning process. The exact form of this comparison will depend on your organisation’s process for preparing and presenting its corporate plans. What is important, however, is that each element of the corporate strategy should be checked against the long-term marketing plan to determine its validity and robustness.

Decide strategic changes. Emerging directly from the previous stage there will be a clear identification of the divergences, if any, between the two types of strategy. This will immediately

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highlight the nature of any changes to be made. These should then be addressed, item by item, to determine what changes should be made in the overall corporate strategy. l

Translate to action. The final stage of any planning process should always be to do something. The action may be to incorporate these changes in the overall corporate plan. The more thorough alternative is to produce a separate action plan where the shorter-term elements of the revised strategy are translated into the necessary actions with related time-scales. Ideally, the whole long-range marketing plan (PROACT), and the robust strategies it contains, should be no more than six pages long. If it is longer than 10 pages it may not be read.

The way in which the scenario analysis was used to refocus corporate strategy in our case study company is shown in Example 15.1.

EXAMPLE 15.1 CASE STUDY: STRATEGY FORMULATION Over the next year, the company made two important strategic decisions. 1 In recognition that its industry was maturing, it would move away from the high growth strategy that it had pursued for the previous 20 years. It would focus much more on cost control to improve the quality of financial performance from the existing level of business. It would freeze overall R&D spend, reduce its head-office staff by one-third and close several of its overseas subsidiaries. 2 It would establish a biotechnology research unit at the California research station. This would be funded by reducing the number of organic chemists at the UK research station.

This case study illustrates how scenario analysis can be used as a powerful tool in strategic planning. Its main virtue is that it encourages managers to think outside the square, and it leads to uncomfortable and provocative discussions. Most importantly, it can bring a real insight into the future of the sector and thereby enable an organisation to improve its competitive position substantially. The case study also illustrates how â&#x20AC;&#x201C; even after effective environmental analysis and scenario analysis â&#x20AC;&#x201C; strategic decisions can still be hard to make. Finally, it shows how individual opinions, position power and internal politics can all significantly affect the final outcome. To show that scenario analysis is not simply an academic exercise, the outcome of the case study company is described in Example 15.2.

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EXAMPLE 15.2 CASE STUDY: POSTSCRIPT The first strategy shift – to move from growth to consolidation and to reduce the cost base – was not accepted by the middle managers of the organisation. The company was enjoying the most profitable year in its history, and there was no recognition of the need to change. The Board backed off its plans, and the company carried on as before. The profitability of the European market declined steadily, and in the mid-1990s the company was obliged to make swingeing cost cuts involving large numbers of enforced redundancies. Profit levels never returned to the 1987 level, and the company has now been spun-off by its parent. The second strategy shift – investment in biotechnology – was carried through. The company invested in-house rather than in outside laboratories. It is too early to pronounce on the success of this strategy. The company seems to be well placed, but biotechnology is still not making a contribution to profits, and there is growing concern about the acceptability of genetically engineered material in agriculture.

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16 CONCLUSION: SCENARIO PLANNING

CONCLUSION: SCENARIO PLANNING

In Section 11.3 we identified three types of uncertainty: random uncertainties, hidden certainties and expected outcomes. Despite all the techniques we have presented in this unit, there really is nothing you can do to detect genuinely random uncertainties. However, genuine wild-cards are relatively few in number. Some day a comet will destroy civilisation as we know it – but we do not know when, and there is little that we can do to prepare for it. At the other extreme there are hidden certainties, which are readily predictable once we have identified them. We have the planning tools we need to handle hidden certainties and the many other trends that are currently hidden from us. The largest group of all, the expected outcomes, are not totally predictable but can be handled by the use of scenario forecasting. These were the focus of the second part of this unit, since they represent the most important category of uncertain elements bedevilling long-range planning – and yet they are the least well understood by managers. As we said earlier, the best way of understanding scenario forecasting is to do it, and we hope the activities here have tempted you to do this. The appendices include some further examples of scenarios. ��

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APPENDIX 1: THE FUTURE OF AMERICAN BUSINESS SCENARIOS This first set of scenarios were prepared by Global Business Network, a consulting firm based in Emeryville, California. They were commissioned by the Journal of Business Strategy and were published in its November/December edition in 1997. The authors were Peter Schwartz, Lawrence Wilkinson and Sean Baenen. The authors begin by identifying four undeniable factors (what we have called hidden certainties) that they think should underlie all views of American business: l

The population in the USA will continue to grow dramatically over the next half century.

People will live longer and work longer.

The role of women will continue to expand.

There will be the rise of the global teenager.

They then developed four scenarios, which are reproduced below.

SCENARIO 1: CHANGEOVER AND THE LONG RUN A world characterised by:

Ten more years of turbulence as the world economy continues

its transition

Flexible enterprises playing in fluid markets

December 2007: Remember how confusing things used to be for businesses in the 1990s? The pace of change was outlandish. Technologies continually remade themselves, markets around the world continually redefined themselves, and industries continually reshaped themselves. No organisation was immune from radical change, and there certainly wasnâ&#x20AC;&#x2122;t much in the way of sure bets. Well, nothingâ&#x20AC;&#x2122;s changed. Those who found the 1990s a confusing time have found the early years of the 21st century to be no different. The world continues down a path of economic and social transition that will determine the way all of us work, live, and play for the next several decades. The problem is, nobody is sure when that decision is scheduled to be handed down, and there are still no clear indications of who will be making it. Every time Microsoft, for instance, seems to have its thumb over the computing industry, something else comes along to change the rules of the game. In 1994 it was the Internet and Netscape. In 1996, it was Java. Four years ago, it was the widespread release of

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smart appliances and silicon-driven objects (ubiquitous computing). Now, it appears that micropenny transactions – on-line transaction systems that allow vendors to sell goods on the Internet for less than a penny – will be fully up and running on the Internet by mid-year, throwing yet another paradigm into the mix of has-beens. Nobody can seem to open their corporate arms wide enough to grasp the breadth of opportunities incessantly presented, remade, and then destroyed by the world’s markets. Many economies around the globe make great games to play – it’s just that the winners of the game seem to change quarterly. Some are being left out as they struggle with religion and politics, but many others have made remarkable progress toward free markets. Western economies are fluctuating but, on balance, appear to be growing right along with consumer confidence. What is left in the wake is opportunistic chaos. Things are changing so quickly that nothing works truly efficiently with anything else. We’re left in much the same situation we’ve been in for the past 15 years. With no emergence of widespread standards (either in technology or terms of trade), companies are left to remake themselves into flexible opportunity finders: organisations that can play across a number of fronts, recognising the rules of the game could change at any moment and market defeats will often be as common as market successes. The need to stay fluid has forced companies to become as ‘core’ as they can become. Many of the things corporations used to do up until the late 1990s – build and run their own information systems, payroll functions, customer service, and human resources – have been outsourced and are now considered infrastructural. Starting with Shell Oil’s decision to shed almost all non-essential operations in 1998, this industrial delimitation of traditionally integrated business process has changed the rules of competition several times over. (For instance, with just about all retailers using the same inventory management system, they can no longer compete on the basis of inventory control.) Companies trying to lock consistency into a particular industry or value chain are finding it a challenge when environmental forces change the fundamental nature of the industry itself every couple of years. If the constant renewal of the corporation from top down weren’t enough, the past decade has armed consumers with powerful technologies that are giving them more leverage than they’ve ever had before. The Internet has grown up quite a bit since the inception of the World Wide Web in the early 1990s. Looking back on it, Andersen Consulting’s move into the market for consumer agent software in 1999 seems to have been the push that opened the Internet’s floodgates and shifted the power in the channel decisively to the customer. Today, 25% of all commerce is done over the Internet, most of this through so-called ‘personal RFP agents’ that essentially broadcast a potential buyer’s request or proposal for a given product or service into the ethers of the

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network. In this on-line world, it is the onus of the vendors to be exceptionally good listeners and to respond to customer requests in a timely, cost-effective, and often highly tailored fashion. In today’s world, vendors aren’t chosen, they’re invited. This environment has exacerbated the bifurcation of markets and industries that began in the early 1990s, creating a market atmosphere where extremely large and extremely small organisations thrive and coexist in the same value chains. Consolidation continues in downstream organisations where there is a paramount need for capital and R&D. Meanwhile, those organisations upstream continue to fragment as the need for creativity and speed becomes ever more important. The motto: Either get big or strip down – there’s no room in the middle. Again, it’s not because there are so few winners in this economy. Quite the opposite; there are far too many. The half-life of success ensures short-lived victories and inevitable unseating by an oscillating industry structure and a marketplace full of challengers. Management dogma today will be management dog food tomorrow. What’s considered law in business is truly no more than informed suggestion, as the entire game could change in a month’s time. The best you can do as a planner is surround yourself with good people and create an organisation that at its core can learn faster than those it competes with. And that’s exactly what companies are doing. With such a short span of success, executive talent is at an all-time premium. Most of the corporate elite are represented by talent agencies such as International Management Group. Shareholders are willing to pay extraordinary salaries to executives who can maximise success in markets characterised by almost zero sustainable competitive advantage. IMG, which began representing Louis Gerstner when he retired from IBM to become an unrestricted free agent last year, just negotiated a 5-year, $65 million deal for him with 3Com. Much of that money came in the form of stock options and a signing bonus to keep 3Com’s board of directors under the salary cap imposed by last year’s Gebhardt Antitrust legislation. The challenges Gerstner will take on in his new role are the same that all companies face as they approach the end of the first decade of the new millennium. The rules are new and changing. Opportunities are pointed and moving. The past 10 years of the digital age have been compared to a number of things, but perhaps nothing more accurately depicts the business climate than the analogy of the roller coaster ride. The marketplace has twists and turns. The view changes around each bend. Some choose to enjoy the ride, finding its unexpected high and lows to be part of the fun. Others are quite anxious about the loss of control and inability to see the larger picture. In markets, as with the ride itself, those looking for excitement seem to have fared much better than those looking on in dread.

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How companies can prepare l

Reinvest in external relationships to enhance learning through collaboration. Invest in partnership technologies to stay loose and flexible.

Commit to training and educating your staff. Instead of promising a job for life, attract employees with the promise of a future, even if it’s not with your company.

Recruit and build good leaders who excel in innovation, improvisation, partnering and coaching.

Prepare for a short half-life of success by building an organisation optimised for learning from both success and mistakes.

Tune your capital structure so you can fail and ‘fail smartly’, as market defeats will come as often as market victories. Know your customers and pay attention to their values. Seek opportunities for communication and co-production.

SCENARIO 2: THE LONG BOOM A world characterised by: l

Two decades of sensational economic growth fueled by infrastructure growth and consolidation

Organisations competing in truly open markets

December 2007: Wired, the world’s leading news magazine, recently published its yearly ‘Numb Minds Awards.’ With the Dow Jones industrial average crossing the 18,000 mark in September of this year, the awards were given to various pundits that had a decade prior predicted the decline of America – indeed, of Western Civilisation. It seems funny, almost. In the 1980s and 1990s, we heard endless diatribes about how the world was falling apart and how much more difficult our children’s lives would be from our own. So said many poets, politicians, and scholars. In fact the turbulent ’90s were first stages of an economic boom that has lasted longer than any in history. And it shows no signs of ending. The world’s economy has essentially doubled during the past 12 years, bringing an increase in prosperity for billions of people. Today, we are riding the waves of what many believe to be at least a 25-year run of a greatly expanding economy. In retrospect, we can see the seeds of this global expansion in two core technological driving forces that began to emerge in the early 1980s: the introduction of personal computers and the breakup of the US Bell system. The economic boom of the mid-1990s was directly traceable to these phenomena, but only as we look back on the late 20th century can we see the whole story. First, the computing wave: in the mid-1990s, processing technologies had extended beyond the PC and into homes, tools,

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and cars. Some thought that would be the extent to silicon’s realm. By the turn of the century, however, Intel was providing silicon smarts for everything from airplanes to pens. With processor speeds doubling every 12 months or so, new applications for computing began to emerge. Today, even former conundrums such as handwriting and speech recognition are a breeze in even the smallest of devices. Early press reports say that Intel’s P12 chip – due for release later this year – will have more than a billion transistors, bringing that much more power to the shiny silicon wafer. While it might not have looked like this when you were living through it, the trajectory for the telecommunications wave followed much the same arc. The breakup of the US Bell companies in 1982 led to an all-out frenzy of entrepreneurial spirit in the communications industry throughout the late ’80s and early ’90s. By the late ’90s the rules of telecom underwent yet another quantum change when the value in the network shifted from moving voice to moving data. In 1998, the rules changed again with a widespread move to wireless. Mobile phone systems and all-purpose personal communications services arrived first with vast terrestrial antennae networks and, soon after, with the big satellite projects (Teledesic and Iridium among them). In 2002, Teledesic’s Global Internet Network went live, allowing truly seamless connection to any information infrastructure from anywhere in the world. In 2007, high-bandwidth connections became cost-efficient with the price war between Concert and Bell Atlantic. Video can now easily be moved through standard communications service in developed countries, making the videophone the hottest selling item during the 2006 holiday season. The symbiotic relationship between these technology sectors led to a major economic discontinuity in 1997, one that would transform the global economy for the next century. Paul Romer and today’s other leading economists call it the ‘Big Bang’ – that moment when there was an almost immediate fueling of substantial economic growth both in the traditional sense of job creation and in less direct, but utterly profound, ways. For instance, hardware and infrastructure companies experienced a boom as building the new information network became the global business opportunity in the years between 1999 and 2004. A new media industry also exploded on to the scene to take advantage of interactivity, customisation, and other unique capabilities of the network. And after a few fits and starts, the Internet has become the main communications medium of the 21st century for data, voice, video, and whatever other communications services you may like. With this technological, economic, and network prosperity came the development of on-line commerce. Visa and American Express forms of eCash in 1999 gave electronic money widespread acceptance by the turn of the century. It wasn’t long before 144

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businesses began selling everyday goods on the ’Net. By 2000 on-line revenues had crossed $10 billion. Last year, 40% of all groceries were teleshopped, and fully half of the economy moved across the wire. Along the way, completely new types of work have been created. The old style in-betweens, such as retail category killers, were cut out and new types of intermediaries, such as high-experience retail shops (where the thrill of shopping counts for more than the goods themselves), connected buyers and sellers. Sure, jobs have been eliminated, but so many new ones have been created by a substantially more efficient marketplace that most studies show the normative quality of life rising around the world. With the friction taken out of the system, savings in the form of excess capital are channelled into new ventures, creating even more opportunities to work. Those two technological forces (computing and telecommunications) did much more than revolutionise the world economy – they changed the world’s stage. By the close of the 20th century, Asia was booming and developed Western nations were forging ahead on a path of technology-led growth, showing the benefits of developing markets and freeing up trade. Individually, nations began adopting the formula of deregulation, privatisation, opening up to foreign investments, and cutting government deficits. To ensure they wouldn’t miss the boat, even formerly isolated countries began to sign international treaties and agreements which, in turn, accelerated the process of global integration and fueled the long boom. Key among these was the Information Technology Agreement of 2000, which let all countries trading in IT abolish tariffs by 2002, and the global telecommunication accord, in which 70 nations signed a pact to rapidly deregulate their domestic telecom markets, allowing foreign access for developed countries and leapfrogging for emerging economies. The long boom shows no signs of slowing. Not only is the world more prosperous, but, dare we say, people around the globe genuinely seem happier with the fundamental changes in day-to day living introduced by technology and the new ethos in openness among the world’s governments and economies. The young technologies we saw emerge in the late 1990s have hit their adolescence and young adulthood running. So have the constituents of the global community. And with promising breakthroughs during the past six months in biotechnology and nanotechnology, the sky appears to be the limit for human ingenuity. Where it stops, nobody knows.

How companies can prepare l

Prepare for the networked economy by investing heavily in on line experience with suppliers and customers. Concentrate on investments that leverage the network, not in the infrastructure itself. (For instance, the telephone business is a good business,

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but the businesses around the telephone are incredible businesses.) l

Invest heavily in your brand, because it will give you immediate exposure and competitive advantage in emerging markets.

Begin designing new classes of products and services that will

operate in an environment soaked with computing power.

Invest in scale to cover the breadth of opportunities available in the global marketplace. The bigger the better. This will be especially true in Asia/Pacific, as it offers such a large potential market opportunity.

Move away from liquidity and invest with confidence in new businesses and new opportunities.

Push standards adoption in your organisation and in your industry by investing in the new rules.

SCENARIO 3: GLOBAL PALISADES A world characterised by: l

Highly fragmented world markets

Widespread scaling back of formerly multinational companies

December 2007: Many of those who received their Master of Business Administration degrees in the 1970s found themselves trained for a business world that for all intents and purposes no longer existed by the time they were entering the ranks of senior management in the 1990s. Business schools, which needed to update their curricula to reflect the new era of the networked economy and global markets, gradually retooled to churn out the new breed of executive for the 21st century. It wasn’t long before this millennial elite also found themselves operating in a world that was much different from the one for which they had prepared. After almost two decades of hope, the digital revolution never came. Throughout the 1990s and early part of this century, it was one new innovation after another with no sign of the lock-in that so many had hoped would finally come. Each new machine or protocol was expensive and obsoleting, and none of them worked together. By 2003, the Internet seemed to have no real nutritional value, and advanced networking technologies were seen as equipment that could transmit money from corporate coffers to vendors but could do very little to change your life. All of the squabbling over standards and shareholder return in the ’90s and early years of this century made technology complex and just plain expensive – to develop, to acquire, and to deploy. Today, people are much more interested in technology’s impact on medicine, rather than what it does with information. Investment in IT has become incremental, and the once-hot technology stocks are now seen by Wall Street as resembling utilities, with reliable but no longer spectacular annual returns. The slowdown has profound effects on the economy, and indicators suggest that globally there is

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an increasing number of have-nots, a shrinking number of haves, and a larger disparity between the two. While the network economy’s demise was as disappointing as it was surprising, the biggest gap between expectations and reality is on the playing field itself. A generation that had been brought up to believe in open world markets and an open, global economy now works in a business environment that can be described, at best, as multi-local. This has stifled the bullish growth of the mid-1990s, and the foundations are being laid for what will in all likelihood be a new bloc world where cultural and economic exchange are hampered by very real barriers. The reasons for the world’s balkanisation are many and equivocal. In retrospect, it’s easy to say an open world was simply too idealistic. After the end of the Cold War, countries of the New World Order were left struggling to get their own houses into order, much less become a part of a great global community. Regional conflict based on ethnicity and religious creed has flared throughout Northern Africa, Central Asia, and the Caspian Sea. And after 10 years of rummaging, Russia’s 2004 election produced a nationalistic leadership that tightens its grip on the old republics year after year. As these geographies ignited, the West’s delusions of tapping high-growth, emerging markets began yielding to practical strategies of protectionism. But the onus for the collapse of global trade falls just as heavily on the shoulders of those fighting for excise and ego as it does on those fighting for theology and territory. Nationalistic concerns about short-term employment and preservation of cultural integrity in France, China, and large proportions of the Middle East made these nations’ markets practically inaccessible. The backlash was profound. Regional and local treaties have begun to dominate world trade, with hefty excises being levied on any goods moving across regional borders, such as ASEAN to NAFTA. All of this has come at the expense of multinational companies that – instead of opening markets and forming alliances – have spent the past four years stranding assets and trimming domestic operations to bring their companies in line with today’s capped demand. The barriers among nations do not just suffocate the global economy, they prevent the world from getting together in any real way to solve the problems it will take a world to solve. Principal among these is the environment, which continues to be of concern, especially in second- and third-world nations which contain most of the world’s population but are the focus of the least of its attention. Continuing deforestation and rising levels of air pollution are making some areas almost uninhabitable. And the first world isn’t immune. In March of 2005, the US Center for Disease Control announced that the rising incidence of the common cold in North America was largely due to increased toxins in the air and water. Today, it is estimated that as much as 35% of

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the Los Angeles metro population has continual flu-like symptoms because of environmental decay. While nothing has been proven, the so-called permaflu appears to be affecting people in New York, London, and Mexico City as well. Most of us remember that brief window in time – the 1990s – when it seemed that a truly global economy, even a smaller world, was a foregone conclusion. In fact, what appeared to be a desegmentation of the world turned out to be a momentary respite, a step on the way to resegmentation. And that process feeds on itself. Increasing isolationism in the developed world and the lack of new investment in third-world countries is creating a gap between the world’s haves and have-nots that seems to grow almost daily. Instead of global synergy, the world’s leaders are looking to build new empires.

How companies can prepare l

Through co-ownership, such as joint ventures, build a global organisation that is structured as a multi-local rather than a global entity.

Drive costs out of the organisation and concentrate on operational excellence. Costs will undoubtedly rise as low-cost labor pools are isolated from outside markets.

Develop a government affairs division that can devote full-time resources to lobbying the governments and militias that regulate the flow of goods and information within, and through, their borders. Invest in the game being played.

Invest heavily in services, because borders are much more permeable to services than they are to products.

Build efficient information systems, since proprietary networks will be a source of competitive advantage.

SCENARIO 4: WILD CARD SCENARIO – WET, ARCTIC, AND BLUE A world characterised by: l

A fundamental shifting in the global climate

An incipient change in the locus of industry and tenor of world politics

Wild Cards are those seemingly sudden developments that have the power to change the outcome of the entire game beyond all recognition. Because Wild Cards are so uncertain, it’s not practical to count on their arrival. As such, they are not futures we plan for, but against. During the 20th century, and particularly the past 20 years, Wild Cards (the quick fall of communism, increasing number of natural disasters, and the rise of the Internet and its current concomitant economy, to name a few) seem to have been occurring with

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increasing frequency and increasing importance. As with the last three scenarios, we ask you to suspend your disbelief and contemplate this Wild Card, which may at first appear to be unrealistic, but is proving to be plausible. December 2007: The world today is subdued by a profound global climate that can essentially no longer be predicted. What most of us believed was simply another inconvenient El Niño, the winter of 1997–98 turned out to be the precursor of large-scale climate shifts that have left weather patterns nearly impossible to forecast from year to year. The only thing we do know is that it will never be the same. The North American grain belt has seen five severe droughts over the past decade, while – despite its many dams – the Colorado River floods seemingly annually from heavy rains in the Arizonan and New Mexican deserts. The fisheries of South America have been reduced to shambles, thanks to Pacific tides that oscillate unpredictably from month to month, and much of the marine cloud layer in the western Pacific has disappeared, warming water and raising already muggy temperatures in the region by as much as 10 °F. Perhaps more dramatically, and more telling, is the settling of Europe’s climate to an average annual temperature some 15 °F lower than average levels of the late 1990s. As Mother Nature perfects the new meteorological foot-print she plans to leave on the globe, the entire world is on the verge of destabilising while populations and economies begin to contemplate the permanence of the changes and scramble for relief. A tense time indeed. Why couldn’t we have seen this coming? Well, in truth, we could have. In fact, many did. Any keen student of meteorology or geology could have told us of the volatile nature of the Earth’s climate throughout this planet’s history. The simple fact is that intervals between glacial periods (the world’s climate for the 10,000 years leading up to what now appears to have been 1998) represent less than 10% of a global ice-age cycle. The other 90% is characterised by dramatic instability, followed by an ice age, as glacial movement and reformation wreaks havoc with the world’s weather. Unfortunately, recorded history began within that 10,000 year window. This cycle has occurred many times in Earth’s history, but at 8000 BC the last period ended, long before the onset of even the earliest civilisations. The United States has seen its once-mighty global agricultural machine slowed dramatically with a continental climate that can be described as angry, at best. New York’s winter last year could be compared to San Francisco’s. Much of the lower Eastern seaboard is hammered regularly by violent storms, and the winds and flooding have reduced the agricultural output of the Southeast and slowed the development of the ‘New South’. While the fertile soil of the Midwest is dryer than at any other time on record, rains have poured on the desert Southwest. For the past two years, Southern Californian summers have been extraordinarily humid,

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with residents comparing the weather to Miami’s, complete with eastern Pacific hurricanes. The warming of the US has dramatically shaken many industries, everything from energy companies with large natural gas investments to union HVAC contractors in Chicago. It has even threatened the food supply. In 2004, then-President Kemp made maintaining the US role in global food production one of his top priorities. One of the first steps was to begin experimental moving of topsoil to the desert. While this provided some excellent artificial farmland, the metals from the massive sand runoff are polluting the soil and choking the rivers. In any case, America can concentrate only on its domestic population and preferred trading partners. Sadly, there are signs that even that support system is weakening. But it’s not just the US that has been put in a frenzy. In fact, the Americas seem to be getting off easily in comparison to the rest of the world. The Gulf Stream, which used to warm arctic air as it moved across the North Atlantic into Europe, began to shift in late 1998. It now appears to have taken up permanent station along the southern coast of Spain. This has done nothing less than shake up the climate of the entire continent. Europe as a whole has seen average temperatures drop some 12 °F. Snowfall is now a common occurrence in Britain and places like Finland and Norway have become desperately cold and icy. While ice sculptures in the Tuilleries were a nice draw for a time, tourism has dropped off substantially, as summers have become almost one month shorter. As Europe suffers through what is essentially its third year of Canadian weather, its constituent countries are beginning to get desperate. Populations willing to hunker down for what was presumed to be a freak long winter followed by a shorter and shorter summer are now getting restless. The wheat fields of Britain, France, and Germany are being pushed to their limits, and reliance on foreign grain is at an all-time high across the European Union. Further droughts in the US have third-world countries (with their new, longer growing seasons) extremely nervous. What the Europeans are currently lacking in warmth and food, they can more than make up for in combat aircraft and ambition. France is already talking tough to its former colonies in central Africa, and Turkey has stepped up naval patrols to further ‘exercise territorial rights’ on fertile Mediterranean islands – islands also claimed by Greece and Italy. The long-term effects of this massive shift in global climate are only now being felt. As Mother Nature makes clear that the climate shift is the norm rather than a spike, countries are positioning themselves for the next millennium. Under current practices, a large part of the Western world will be pressed to sustain itself with food, and the concomitant shifts in population promise to strain already tense cross-border relations. If a true Ice Age is on the way, then the 500 million people of Europe will be permanently displaced. And while the burning of fossil fuels, oceanic pollution,

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and deforestation did not bring about this change, they will likely keep us in such a period for that much longer.

How companies can prepare l

Plan for a recession and develop a resource structure that can be plowed into the company during crisis.

Look at investments in land and resources in the Asia/Pacific region. At the same time, invest heavily in domestic markets, since this type of global change will create an environment similar to that created by physical trade barriers.

Build a flexible organisation that can shift tasks from node to node as seasons change.

Deepen your pockets, because this time of readjusting will require both cash reserves and good credit.

Scan the environment and look to others chasing futures that sound like Wild Cards to your organisation.

Relocate operations away from coastal areas in the southeast and southwest and below the 52nd parallel.

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APPENDIX 2: THE FUTURE OF COMPUTING In 1998, an article in The Economist magazine speculated on the future of personal computing. This article, from the issue of 12 September, is reproduced here.

AFTER THE PC Whisper it quietly, the personal computer – the machine of the 1990s – will be entering its twilight years by the beginning of the new millennium. It might be thought absurd to suggest such a thing when Microsoft, the company whose software has done most to define the PC, is about to be hit with the biggest antitrust action for a generation; and when PCs have become commodities sold in supermarkets for little more than the cost of a fortnight’s groceries. Yet precisely when the PC appears to have reached its apogee, its days of greatness look numbered. Nor is this just the wishful thinking of those companies eager to dance on the grave of the ‘Wintel’ monopoly. IDC, a forecaster of computing trends, recently issued a report entitled: ‘The end of the PC-centric era’. The high-tech consultancy Forrester Research has similarly declared the PC era to be ‘winding down’. Even Bill Gates has expressed the fear that the PC might ‘suffer the death of a thousand cuts’. Users are not about to desert the PC altogether. But many will find that their computing needs are better met by specialised devices and appliances that take advantage of Internet standards, the Java programming language and lots of cheap processing power. These appliances will offer the reliability and ease of use that PCs have failed to provide. They will offer access to the Internet or connection to a firm’s wide-area network (WAN). Many will use broadband wireless to provide mobile connectivity. They will take a variety of forms: television set-top boxes, fixed-screen telephones, smart mobile phones, handheld computers, personal digital assistants (PDAs) and network computers (NCs). From almost nothing, IDC predicts that the sale of such appliances will nearly match that of PCs in 2002 and thereafter greatly exceed them. From that point on, Forrester expects the PC to go into gentle, absolute decline. IBM, a likely beneficiary thanks to its expertise in computer networks and its big mainframe servers, calls the dawning new era ‘pervasive computing’. That is to say, computing that can take place anywhere, any time. Whereas the PC is a generalist, the strength of the new appliances will lie in specialisation. Set-top boxes used to decode digital broadcast signals will also use a handset to provide Internet games,

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Web enhanced television and home shopping. This is not the old notion of ‘convergence’ of PC and television, which has always excited vendors more than consumers. It is rather about unifying digital and interactive entertainment around the television. Fixed-screen telephones will provide home banking, e-mail, data from the Web and every kind of e-commerce. Mobile phones will be a platform for corporate sales and service applications, for transferring data and for managing company supply chains. NCs and PDAs will give their users access to databases and sophisticated Java-based business applications with a minimum of fuss. Devices inside cars will provide traffic information, text-to-speech e-mail and send real-time data to sales departments and fleet operators.

I love my PC, I love it not The PC will lose its status as the universal computing solution

because of its own failings and the promise of new technologies.

The PC is just too complicated for most of the computing that users

want at home. Applications such as Microsoft Word or Quicken are

excellent if you want graphics-rich documents and sophisticated

financial management. (And people who want such things will own

at least one PC.) But for sending and receiving e-mail, playing

games or Web-surfing, the PC is cumbersome, unstable and slow to

boot – and usually in the wrong room.

PC household penetration in America seems stuck at about 40%.

Not many home users approach their machines with confidence,

other than for the most routine tasks. When PCs run new

applications successfully, most people feel relief and almost pathetic

gratitude – a standard of reliability tolerated in no other consumer

product.

Within firms, the same frustration is tinged with urgency. Forrester’s

Carl Howe argues that the PC’s one-size-fits-all approach to

computing cannot satisfy the requirements of business on the

Internet. To achieve the ‘always open’ availability that e-business

demands, functions once performed by PCs will have to be spun

off to specialised appliances. Firms will install vast storage boxes

for holding data and single-task servers running crucial applications

that require almost no maintenance. Users will be able to choose

how they connect to the network from myriad appliances – from

Palm Pilots to giant conference-room computer whiteboards.

The existing PC client/server model has gone beyond its natural

limits and the mess is evident in nearly every large firm. At first the

idea of linking PCs in networks served by a powerful central

computer seemed splendid. Users could wrest control of their own

computing from central IT departments. But it has meant a

nightmare of complexity and little accountability from vendors. The

PC’s much-vaunted adaptability is partly to blame. Robin Bloor, a

consultant at Bloor Associates, says, ‘The reality is that the PC is

ultimately inappropriate for corporate computing. The term PC

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means personal computer and hence personal ownership of the device and the software – and personal control.’ Server-based operating systems (Windows NT, various species of Unix and mainframe operating systems) cannot talk to each other. The storage of vital data is increasingly anarchic. Irving Wladawsky-Berger, who heads IBM’s Internet division, says that what has been built is the computing equivalent of the Tower of Babel. Equally fundamental, PC client/server systems cannot reliably support the WANs that big firms use for their private intranets. Mr Howe says: ‘While rebooting once a day is a fine workaround for a departmental application, it falls short of the four-hours-a-year downtime standards set by Unix and mainframe systems. On the Internet, a competitor is just a click away – system failure equals lost revenue.’ The new appliances depend upon cheap and powerful processing power. The handheld ones of today can nearly match the speed and memory of two-year-old PCs. But perhaps the decisive factor is the set of universal communications standards for connecting clients to central servers that has been established by the Internet. Mr Wladawsky-Berger thinks pervasive computing is a technological development that is inevitable, no matter what individual companies do or how governments legislate. He compares computing today to electricity in 1900. Before the grid, generation was local, unreliable and noisy. The Internet is, in effect, a grid that dispenses applications rather than electricity. Thus computing based on the Internet is like a grid designed to deliver information and resources as reliably as power companies deliver electricity. In Mr Howe’s vision, platforms based on operating systems are irrelevant. The ‘computing utility’ will separate storage from servers. Data will be stored in ‘an online armoured truck with a mean time between failures measured in decades.’ Internet protocols will link the storage to the servers, including some that perform a single task, such as running e-mail. Like the devices they are connected to, these appliances will achieve reliability and lower costs by being designed to do only one thing well. Who will be the business winners and losers from the end of the PC era? The biggest winners will be appliance giants such as NEC, Hitachi, Sony and Philips. The hardware-software synergy needed for appliances plays to their strengths. Next-generation appliances will look more like stereo equipment than computers. More than half are likely to be Japanese made. In the consumer market, the Japanese will also be strong. However, smart telephones could be dominated by Nokia, Ericsson and Motorola, which recently announced a joint venture with Psion to establish the British handheld computer specialist’s EPOC operating system as ‘the standard for wireless information devices’.

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The most glaring threat is to the PC makers. As IDC says, any suppliers to the PC market ‘must develop strategies to participate in the appliance segments of risk marginalisation.’ Few seem to be paying attention. Some are looking at the Windows Terminal market – a network computer or ‘thin client’ that runs off a version of Windows NT for servers. Others are sticking their brands on PDAs made by 3Com and Casio. But most seem to be waiting for Microsoft and Intel to give a lead, while they concentrate on being profitable in the age of the sub-$700 ‘good enough’ PC. The danger is that ‘wait and see’ means giving up mind share and market share as appliances cruise past PCs in five years’ time. An exception is Taiwan’s Acer, which is developing a range of digital equipment that it calls the XC or application-specific computer. Acer has developed five different platforms, bundled with applications as users require, from which a variety of Internet access devices can be spun off. Acer’s boss, Stan Shih, believes that the PC market will ultimately decline. He wants the XC to help intellectual property and services replace hardware at the core of Acer’s business. Mr Shih says he has not been so excited since he began proselytising for the microprocessor 2 years ago. For Intel, the problem is not quite so acute as it is for some of its customers. Its forthcoming 64-bit Merced super-chip will power the hordes of big servers that will connect to the Internet, boosting its sagging profit margins; and it is already active in the appliance market. But a slowing and eventually declining PC market will still be bad news. Rival chip makers such as Texas Instruments and Motorola, which are strong in digital signal processing (a technology used in mobile telephones that is particularly suitable for intelligent appliances), welcome the chance to compete in a market that Intel does not dominate. The sheer size and diversity of the chip market will allow more than one winner, but margins will be as thin as a silicon wafer. For software companies there are both dangers and opportunities. Margins on software embedded in the appliances will be skinny. But the rapid growth of appliances will mean that many more people will be doing more computer-related things more of the time. Equally positive for smaller software firms is that the fastest growing part of this expanded market is unlikely to be controlled by Microsoft. Suppliers of the business applications used by large firms, such as SAP and Oracle, thrill to the prospect of putting their software on Web-servers and reaching all those new users. Oracle’s Larry Ellison, in particular, is a well-known supporter of the trend towards Internet computing. He says: ‘Client/server is an evolutionary dead-end. With professionally managed centralised servers, for example an applications server and a database server, you can make the WAN ten times more efficient.’ The latest release of Oracle’s range of packaged applications will not even run on client/server. Oracle’s Network Computing Architecture (NCA) consists of three tiers – a database, an OU BUSINESS SCHOOL

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applications server and a thin client (an NC, a cheap PC or, indeed, any appliance with a Java-enabled browser) – all based on Internet standards. Mr Ellison concedes that tumbling PC prices have taken the momentum out of his crusade to put an NC on every desk. He is embarrassed to be reminded of his claims of a couple of years ago that the PC was dead. Yet he maintains that, when customers see the reduced costs and improved efficiency of NCA, ‘this time they’ll get it – I have never been more confident that this time we have caught the inflection point.’ Sun Microsystems, another booster of network computing, makes a similar point, saying: ‘NCs are not meant to be sold as boxes, but as an enabling component in a network.’ Sun is also in a state of high excitement about Jini, a new technology it will release later in the year. Jini, the work of Bill Joy, a Sun co-founder and one of the fathers of Java, promises to allow computing appliances to connect instantly into a universal network. Users should be able to access whatever processing power or features on the network they need – in effect, renting the brains of big computers.

Barbarians at the Gates And then there is Microsoft. A less PC-centric world is clearly one in which Microsoft’s dominance will be reduced. However, despite the best efforts of Mr Ellison, and the diverging computing needs of many big companies, neither client/server architecture nor the PC will disappear. After all, the mainframe is still thriving. In any case, Microsoft seems to be hedging its bets. It has just introduced its version of a server for thin clients, the Windows Terminal Server. For firms that want the benefits of network computing without leaving the familiar Windows user-interface, it is an attractive option. Microsoft is being much more forceful about the underlying software for digital devices. It is desperate for its Windows CE operating system (the initials CE do not stand for anything, the firm says) to become the platform of choice for the consumerelectronics industry. Jon Roberts, who is responsible for CE’s success, says the operating system is not a mere subset of Windows, but a totally new product. Because it is designed to be modular, manufacturers can decide what parts they want to use, even discarding the Windows interface if they choose. Mr Roberts claims that the advantage of CE is that it plugs into a huge community of applications developers. Despite Microsoft’s strength in distribution, others are sceptical about CE. Simple appliances demand simple operating systems. Most appliances will be sold with only a few embedded applications. The search for cheap software will require manufacturers to write their own programs or to license code from little known houses such as Wind River or Integrated Systems. Where CE can score is with quite complicated handheld computers that more closely resemble PCs. The danger for Microsoft, however, 156

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is that CE becomes a kind of NT-lite and so cannibalises much more profitable Windows 98 laptop licences. Microsoft has the talent and money, combined with the strategic readiness of Mr Gates to adapt, as he did to the arrival of the Internet. And even though it recognises the importance of the new appliances, the firm is not about to abandon the PC. Mr Gates knows that, if it is to survive, the PC must become easier to use. To that end, Microsoft is spending many billions in an effort to ‘humanise the user interface’. Anything that domesticates the PC will be welcome, but it is unlikely to prevent a shift of the kind that so many others are predicting. Microsoft is also well aware that pervasive computing will, in time, put an end to the peculiar circumstances of the PC-centric world, in which there were huge benefits to be gained from having just one dominant operating system; and in which ownership of the most ubiquitous operating system automatically conferred domination over the industry. Reflecting on the antitrust action against Microsoft that he has long called for, Larry Ellison grins and shrugs: ‘I guess that maybe the government was five years too late.’ In the new computing world, there will be a plethora of operating systems and appliances, bound together by the common and open standards of the Internet. Many people will still want PCs for their adaptability and for the power they put in a competent user’s hands. But for most people, most of the time, it is likely that practical and idiot-proof appliances will fulfil not only their needs, but maybe even the ultimate potential of computing. ��

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APPENDIX 3: AIR TRAFFIC SCENARIOS FOR THE NEXT TWENTY YEARS Franz Tessun was Vice President, Business Intelligence, DaimlerBenz Aerospace, when he prepared a detailed analysis of the future of air traffic in 1997. He began the analysis with a quotation from an editorial in The New York Times, 10 December 1903: We advise Professor Samuel P. Langley not to endanger his excellent scientific reputation any further by continuing to waste time and money on experiments with flying machines. Life is short and he is able to serve mankind in much better ways than through what might result from attempts to fly. There are more useful occupations for the Langleys of this world!

Seven days later the Wright brothers’ aeroplane made its first flight at Kitty Hawk. Tessun says: l

Any decision with long-term implications is made with a picture of the future in mind. Just imagine long range business decisions with such a faulty perception of the future! How will your picture of the future compare with the actual reality in twenty years?

His article, published in Marketing and Research Today in November 1997, includes two scenarios for the future of air traffic in 2015, and the implications for associated businesses.

SCENARIO A: FLYING IS A PLEASURE IN THE YEAR 2015 It is a sunny autumn morning in the year 2015. Mr. K. is still having breakfast in his apartment in Hamburg, browsing through his newspaper. Today, he can take a little more time because he is starting off on a business trip to Denver. His wife takes him to the Hamburg railway station in her electric minicar. At the station, K. heads towards one of the automatic check-in machines, which look like those used twenty years ago in the supermarkets to collect empty bottles. He places his suitcase onto the conveyor belt, confirming on the display that his luggage should be checked through to Denver. The check-in machine reads this information as well as K.’s flight number from a reloadable chip card, which has replaced the ticket and onto which K.’s secretary has entered the actual booking. K. gets on the Transrapid train taking him to intercontinental airport Berlin-Brandenburg. Thanks to the Transrapid connection, the time needed to reach the airport is negligible. 158

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The Transrapid arrives at the Airport Berlin-Brandenburg half an hour ahead of departure so that K. can directly proceed to the boarding gate. The airport is spaciously designed. An effective passenger and luggage transport and coordination system provides for smooth and continuous operation with short transfer times. From his jacket, K. fishes his blue chipcard ticket. Before long he will have collected enough bonus miles to obtain the red frequent flier ticket. When that happens his suitcase will receive door-toÂ door service. Twice a year he will be entitled to upgrade to the separate â&#x20AC;&#x2DC;Flying Lounge Classâ&#x20AC;&#x2122; airplane. These planes operate on highly frequented routes, parallel to the normal planes, but offer all passengers first class service. When he reaches the service terminal, K. again verifies his seat reservation with his chipcard. Realising that, meanwhile, one of the seats by the emergency exit is free, he changes the seat himself, because he prefers the greater legroom of this row. The flight is called and the passengers press along the pier to the departure lounge. A crowd of Japanese business men rush into the lounge with business documents in their hands and jackets over their arms. Presumably they were sitting in the Airport Congress Center and had just realised what time it was when their flight was called. K. moves towards his seat, takes out the business documents for his trip to the United States and Asia from his briefcase and looks through them again in a more mechanical than concentrated manner. He stores the majority of information in his small laptop PC, which he opens and connects via cable to the modem receptacle. Via modem, he downloads to his laptop information received yesterday afternoon by his office computer in order to check if some late notes regarding his trip have been sent. K. is to discuss organisational details with the company representatives in Denver and Pusan, as his company is about to move its headquarters from Europe to the economically more interesting markets of NAFTA and Asia. He stows his computer, makes himself comfortable and, to kill time until his meal is delivered, begins playing a video game in his seat display. K. still cannot get used to the automatic catering which rolls up and down the aisle, stopping at each seat. He finds this too impersonal and often dreams up some special request, so that a stewardess comes by in person. After his meal, K. again pulls out his ticket chipcard and puts it into a slot on the side of the video display on his seat. He has a second look at his route and finds out that his company has not paid more for the stage to Denver than he had paid for his private trips. Would it perhaps be possible to save money for his company making use of one of the many other connecting flights between the United Stages and Korea? Directly from his seat and for the most part free of charge, K. would be able to change reservations. Because of the many smaller Asiatic carriers serving the Pacific area, this investigation is far too much trouble for him and, tucking

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his chipcard away in his jacket, he selects the channel on which the flight details are transmitted directly from the cockpit. One movie and several rounds of ‘video games’ later, the flight punctually arrives at Denver Airport. In the arrival hall, there is no need for K. to seek his bearings and as a matter of routine he heads for one of the service terminals. With his ticket and credit card and at a charge of $10 he has his suitcase brought directly to the hotel so that he does not have to collect it from the belt (since there are no longer any customs checks between Europe and the United States). The short meeting that K. is to attend the very same day takes place on the other side of the city. K. is just about to hurry in the direction of the local train when the loudspeaker sounds out: ‘We are sorry to announce that due to technical problems, the city transit line is out of service for three hours. Please contact our staff for information on ground transportation. Thank you.’ K. knows that it is not advisable to take the bus or a taxi since the rush hour is setting in, turns round on the spot and, at the service terminal, for $25 books a seat on the airport helicopter shuttle service. With a triumphant smile on his face he forces his way through the crowd of the other passengers still standing around in a rather forlorn manner after their arrival from Berlin.

SCENARIO B: FLYING IN THE YEAR 2015 – A NIGHTMARE It is 6 a.m. In the lobby of his hotel in Pusan, K. is waiting for a taxi to pick him up. He has got a long flight from Korea to Europe ahead of him. He is bound for London, where he has to report to his company’s headquarters before returning home to Hamburg in Germany. As they have reduced the nonstop flights from Pusan to London to two per week, he has been stuck in the city for a day and a half after his conference has ended. In fact he could have left Pusan the day before yesterday, immediately after the last lecture, in which case he would have only got to Seoul on a Korean National Air flight, and then there would have been an almost twenty-four-hour wait for their connecting flight. ‘I’d rather have the extra day here in Pusan and the nonstop flight with American Global Airlines’, he thinks. With the rush hour setting in, the taxi is twice held up in short traffic jams on its way across the city to the airport. K. is starting to get restless because a glimpse at his watch tells him that he can now only hope and pray to reach the airport just in time to comply with the two-hour deadline for his check-in and thus not lose his reserved seat. K.’s command of Korean is rather scanty and so he is really surprised when he realises that the taxi driver has politely and smilingly dropped him at the wrong terminal. With a deep sigh of exasperation, K. grabs the handle of his wheeled case and sets out on the long march to the American Global Airlines check-in.

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In Pusan, the American carrier has only a single check-in desk and K. joins the long queue of people already waiting there. ‘AGA announce a two-hour delay in the departure of flight 127 to London’, resounds from one of the loudspeakers. Unfortunately, all passengers travelling in this region now have to face such problems as national protectionism in air traffic has meanwhile reached a stage that, in the event of the airspace being busy, priority is given to the national carrier. It may therefore be assumed that the AGA plane was put into hold for nearly an hour during its approach and was immediately given a departure slot for two hours later. Two hours later, K. has at long last left Korean airspace and has more or less settled into his tight economy class seat. As was to be expected, the plane is not fully booked, so that K. can stretch out and also use the seat next to him. ‘In the last few years the number of passengers has obviously not increased in the way the airlines expected when selecting the seating capacity of their planes’, K. thinks. No wonder in the light of the worldwide recession, high ticket prices and the unattractiveness of flying that airlines are chronically underused and that the financial returns are also poor. In the meantime, lunch is being served. ‘The lacking profitability of airlines is certainly not caused by the catering’, K. reflects, eyeing the meagre offering on his tray. ‘Let’s hope they are not saving on maintenance like they are on the catering. Who knows how long components in the aircraft will last, considering the give-away prices of aircraft these days!’. The plane suddenly hits an air pocket, which hardly helps dispel his doubts. And then the video display pops out of the backrest right into his chicken salad. After an uncomfortable nap and a few hours of entertainment offered by the onboard video programme, K. realises that flight 127 is approaching England. Despite another delay (which had been caused by air traffic control problems over the territory of the CIS), the plane at last touches down punctually, not taking the delayed departure into account. As darkness has already set in, K. at once spots the illuminated lettering on the Glasgow Airport terminal. The plane has been diverted to Scotland since London Heathrow has suffered a complete shutdown since early morning due to smog. ��

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APPENDIX 4: THE WORLD ECONOMY In its issue of 20 September 1997, The Economist magazine published a survey of the world economy. This included an article on the future of the state, which is reproduced below.

The future of the state Almost any discussion of public policy nowadays seems to begin and end with the same idea: the state is in retreat. At the turn of the millennium, it is argued, governments are confronted by two old enemies, stronger now than ever before: technology and ideology. The state is proving unequal to the challenge. Its power to rule is fading. A new industrial revolution is under way. Advances in computing and telecommunications press relentlessly on, shrinking distance, eroding national boundaries and enlarging the domain of the global economy. Increasingly, these changes render governments mere servants of international markets. Reinforcing the technological shift is a transformation in the realm of ideas, starting towards the end of the 1970s and reaching its climax ten years later with the collapse of communism. That destroyed the system not only in the form practised in communist countries but, more important for those in the West who never experienced it directly, as a sustaining Utopian myth. Judged as propaganda, 1989 did for big government what 1929 did for laissez-faire. Today the main points of this story are almost universally accepted: most tellingly, perhaps, by the world’s political leaders. Left and right, eagerly or reluctantly, bow down before global capitalism. The debate about ‘globalisation’ – whether it is a good thing or a bad thing, whether to embrace it or resist it – is vigorous, but nobody seems to doubt the new power of international market forces. Indeed, the two main sides in the debate agree not just about that, but also about the nature of the balance that societies must strike as a result. In one corner are the optimists: an alliance of modernising conservatives and the new post-socialist left. They regard the triumph of international capitalism as largely a good thing. Governments may have lost some of their freedom to direct economies as they wish, but the world is benefiting from faster technological progress, historically unprecedented opportunities for the relief of global poverty, and greater freedom for millions of people across the globe. Few, if any, optimists doubt that a well-functioning society requires a state that is competent in both senses of the word, which makes the state’s shrinking economic sovereignty something of a drawback. Nonetheless, they find the balance comfortably positive.

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The case for gloom The pessimists – a coalition of populist conservatives, assorted communitarians and the old left – agree on the need for a balance between an effective state and the economic efficiency that market forces can provide. They also agree that market forces, for reasons of technology and ideology, have lately gained the upper hand. But unlike the optimists, the pessimists find this deeply disturbing. In their view, the gains from globalisation are far smaller than the optimists suppose, and the drawbacks much greater. And such benefits as there may be will be divided unfairly within society – a crucial point that the optimists tend to ignore. The new global capitalism, the pessimists concede, will certainly enrich many – but capitalists rather than workers. Moreover, those who fare worst among the workers will be the unskilled, least able to fend for themselves. Globalisation will widen inequality, exacerbate poverty and increasingly lead to social ‘exclusion’. These costs will mount even as globalisation succeeds in its own terms, at a time when government’s capacity to respond is draining away. Its failure to act will undermine the foundations of the democratic state, challenging its very legitimacy. As the remorseless advance of market forces plunges capitalism into a new crisis, Marx will have the last laugh after all. If this survey had to take sides in the argument, it would support the optimists. For reasons to be explained shortly, they are much closer to being right than the pessimists. But both they and the pessimists are wrong in accepting so readily, first, that the state is indeed in retreat; second, that globalisation will play the decisive role in determining the future of the state: and third, that any infringement of the powers of the state must be counted as a cost against corresponding benefits. On the first two points, it seems, both sides share a taste for hyperbole: on the third, they betray similarly collectivist instincts. A more liberal thinker might ask which powers of government have, in fact, been infringed by international markets, and whether those powers, if any, should have resided with the state in the first place. Perhaps the shrinking state is a good thing in its own right, in which case global market forces deserve a more enthusiastic welcome. Or perhaps, despite the spread of capitalism, the state is shrinking more slowly than it should, or not at all – in which case the main thing wrong with globalisation is that it is not doing its job thoroughly enough. This survey aims to sort through these ideas and come to some conclusions about the future of the state. One of its main themes will be that the effect of globalisation has been overrated. For the rich West, both its costs and its benefits are less than meets the eye. For poor countries, undeniably, it makes a big difference to the prospects for economic development. International integration is their fast track out of poverty. With small domestic markets, backward technology and inadequate capital, third-world countries have everything to gain from ending their

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relative isolation and developing close economic ties with the rest of the world. But in the advanced economies, where internal markets are big enough to be fairly competitive, barriers to flows of trade, capital and knowledge have long been lower, and technology is at its cutting edge, there is less to be gained. For these countries, globalisation is a good thing, but hardly a matter of life or death. In the advanced economies, in other words, faster economic growth cannot be imported, it must be built chiefly at home. The issue is not whether governments will hold back the forces of change from abroad, but whether they will resist the more insistent forces of economic change in general. In the rich countries, most of these forces will be domestic rather than international. Most of them, in one form or another, reflect basic economic liberties. Western nations may have reconciled themselves to freer international movement of trade and capital; but that will not oblige them, this survey will argue, to embrace economic liberty within their own borders. The fundamental question for the advanced industrial countries is not, as many suppose, where democracy is compatible with globalisation, but whether democracy is compatible with liberty.

A funny question Even to pose that question will strike many as odd. Democracy and freedom seem two sides of the same coin: how can there be one without the other? The connection meets no more scepticism than the idea that global market forces are in the ascendant. Yet both are dangerous simplifications. Freedom and democracy are linked, of course – but so are freedom and capitalism. And, unfortunately, the pessimists are right to question whether capitalism can continue to get along happily with democracy. They are wrong, however, about the form that a conflict between the two would most likely take. In their view, the danger is that capitalism will in the end prove so socially catastrophic that democratic states will be overwhelmed by protest, leaving fascist or otherwise non-democratic regimes to rise up in their place. Another scenario is more plausible. Democratic states, indulgent of anti liberal values, may make such demands of capitalism, and place such burdens and restrictions upon it, that it will slowly fade away, along with freedom. The emerging polity might still be ‘democratic’ – but that would make it no less dysfunctional and, at the extreme, hardly any less tyrannical. Just now, avowedly left-of-centre parties rule in much of the advanced industrial world, including America, Britain and France. Popular anxieties about globalisation, advancing technology and economic insecurity may be partly to blame. Arguably, though, leftof-centre no longer means what it used to. Bill Clinton, New Democrat, took office in the United States proclaiming: ‘The age of big government is dead.’ Tony Blair’s New Labour is far to the right of the party that lost power in 1979. In France, Lionel Jospin’s 164

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Socialists seem closest to the old model, but even they are shifting a little. For the most part, today’s left-of-centre governments have made their peace with the world of business. They no longer think of themselves as defenders of organised labour or champions of public spending. Words such as ‘enterprise’ and ‘opportunity’ trip lightly off the tongue. These are post-socialist leftists, if they are leftists at all. Perhaps they are no more likely than the conservative parties they defeated to raise taxes and enlarge the role of the state. Many of them won their elections promising to do no such thing. Does this not suggest that the West has, after all, reconciled itself not just to globalisation but also to a greater measure of economic liberty at home? It does not. The so-called realignment of the left falls short of a victory for liberalism. The left, on the whole, may have abandoned its traditional figures of speech and even, to a much smaller extent, its preferred instruments of economic control. But it insists that its traditional values have not changed. From a liberal point of view, the new left’s old values – its enduring collectivism and antiindividualism – are precisely the problem. Moreover, many conservative parties, in their own way, are just as keen on these values as the new left. Appeals to collectivism and antiindividualism are clearly popular with voters, especially when detached from any discussion of the policies needed to put them into practice. Democracy is comfortable with these values. Capitalism is not. But even if this assessment is correct, why is it more plausible to suppose that democracy will undermine capitalism rather than, as the global-economy pessimists argue, the other way round? The answer is simple: because that is what has been happening in the West for the past 50 years. ��

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REFERENCES Aguilar, F.J. (1967) Scanning the Business Environment, Basingstoke, Macmillan. Bood, R. and Postma, T. (1997) ‘Strategic learning with scenarios’, European Management Journal, Vol. 15, No. 6, pp. 633–47. Churchill, G.A. Jr (1999), Marketing Research: Methodological Foundations, Seventh Edition, The Dryden Press. The Economist (1997) ‘The future of the state’, in A survey of the world economy, 20 September, pp. S5–S8. The Economist (1998) ‘The future of computing: after the PC’, 12 September, pp. 93–97. Handel, M.I. (ed.) (1989) Clausewitz and Modern Strategy, Frank Cases & Company. Idon Associates Ltd (1997) Strategic Planning Society Conference, 14 May. Janis, I.L. (1971) ‘Groupthink’, Psychology Today, November, pp. 43–77. Jurek R. J. (1997) ‘The infocritcal eye’, Marketing Research, Spring, pp.38–39 Kahane, K. (1992) ‘Scenarios for energy: sustainable world vs. global mercantilism’, Long Range Planning, Vol. 25, No. 4. Kleiner, A. (1996) The Age of Heretics, New York, Doubleday. Makridakis, S. (1988) Metaforecasting – Ways of Improving Forecasting Accuracy and Usefulness, Fontainebleau, INSEAD. Mercer, D. (1995a) ‘Simpler scenarios’, Management Decision, Vol. 33, No. 4, pp. 32–40. Mercer, D. (1995b) ‘Scenarios made easy’, Long Range Planning, Vol. 28, No. 4, pp. 81–86. Mercer, D. (1996) ‘Industry scenarios – short termism revealed’, Industrial Management and Data Systems, Vol. 96, No. 8, pp. 23–7. Mercer, D. (1997a) ‘Determining expectations of future outcomes’, Technological Forecasting and Social Change, Vol. 55, pp. 155–64. Mercer, D. (1997b) ‘Global forces which will shape our economic and political lives’, Futures Research Quarterly, Vol. 13, No. 4, pp. 37–50. Mercer, D. (1998a) Future Revolutions, London, Orion Business. Mercer, D. (1998b) ‘Long range marketing’, Journal of Marketing Practice, Vol. 4, No. 6, pp. 178–84. Mercer, D. (1998c) Marketing Strategy: The Challenge of the External Environment, London, Sage. (B885 Book 1)

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Mercer, D. (1998d) ‘The future quantified’, Futures, Vol. 30, No. 4, pp. 305–22. Millett, S.M. (1988) ‘How scenarios trigger strategic thinking’, Long Range Planning, Vol. 21, No. 5, pp. 61–8. Moyer, K. (1997) ‘Structuring Scenarios’, Strategic Planning Society Conference, 14 May. Pfeffer, J. and Salancik, G.R. (1978) The External Control of Organisations: A Resource Dependent Perspective, New York, Harper & Row. Popper, K.R. (1959) The Logic of Scientific Discovery, London, Hutchinson. Ringland, G. (1998) Scenario Planning: Managing for the Future, Chichester, John Wiley. Sandberg, K. D. 2002 Harvard management communication newsletter April 2002 & Margaret Mead Meets Consumer Fieldwork Jennifer MacFarland Harvard Management Update August 2001 Schoemaker, P.J.H. (1993) ‘Multiple scenario development: its conceptual and behavioral foundation’, Strategic Management Journal, Vol. 14, pp. 193–213. Schwartz, P. (1991) The Art of the Long View, New York, Doubleday. Schwartz, P., Wilkinson, L. and Baenen, S. (1997) ‘1998 Outlook – the futures of American business’, Journal of Business Strategy, Vol. 18, No. 6, November/December, pp. 40–8. Taylor, B. (1986) ‘Corporate planning for the 1990s: the new frontiers’, Long Range Planning, Vol. 19, No. 6, pp. 13–18. Tessun, F. (1997) ‘Air traffic – quo vadis? Scenarios for the next twenty years’, Marketing and Research Today, Vol. 25, No. 4, November, pp. 229–43. Ulwick. A. W. ‘Turn customer input into innovation’, Harvard Business Review, Reprint R0201H. Wack, P. (1985a) ‘Scenarios: unchartered waters ahead’, Harvard Business Review, September/October, pp. 73–89. Wack, P. (1985b) ‘Scenarios: shooting the rapids’, Harvard Business Review, November/December, pp. 139–50.

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ACKNOWLEDGEMENTS Grateful acknowledgement is made to the following sources for permission to reproduce material in this unit.

Text Appendix 1: Schwartz, P., Wilkinson, L. and Baenen, S. (1997) ‘1998 outlook: the futures of American business’, Journal of Business Strategy, November/December 1997, Faulkner & Gray, Inc. Copyright # 1997 Faulkner & Gray, Inc.; Appendix 2: ‘The future of computing: after the PC’, The Economist, 12 September 1998. Copyright # The Economist Newspaper Limited, London, 1998; Appendix 3: Tessun, F. (1997) ‘Air traffic – quo vadis? Scenarios for the next twenty years’, Marketing and Research Today, Vol. 25, No. 4, November 1997, ESOMAR. Copyright # 1997 European Society for Opinion and Marketing Research; Appendix 4: ‘The future of the state’, The Economist, 20 September 1997. Copyright # The Economist Newspaper Limited, London, 1997.

Figures Figure 12.1: adapted from Ringland, G. (1998) Scenario Planning: Managing for the Future, John Wiley and Sons Ltd. Copyright # 1998 John Wiley and Sons Ltd. Reprinted by permission of John Wiley and Sons Ltd; Figure 12.2: reprinted from European Management Journal, Vol. 15, No. 6, Bood, R. and Postma, T., ‘Strategic learning with scenarios’, pp. 636–7. Copyright # 1997, with permission from Elsevier Science. Every effort has been made to trace all copyright owners, but if any has been inadvertently overlooked, the publishers will be pleased to make the necessary arrangements at the first opportunity.

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